diff --git a/datalab/datalab_session/data_operations/hr_diagram.py b/datalab/datalab_session/data_operations/hr_diagram.py
new file mode 100644
index 0000000..2dc9a49
--- /dev/null
+++ b/datalab/datalab_session/data_operations/hr_diagram.py
@@ -0,0 +1,300 @@
+import logging
+
+import numpy as np
+from django.contrib.auth.models import User
+
+from datalab.datalab_session.data_operations.data_operation import BaseDataOperation
+from datalab.datalab_session.exceptions import ClientAlertException
+from datalab.datalab_session.utils.catalog_utils import (
+  cone_filter,
+  cross_match_one_to_one,
+  extract_calibrated_catalog,
+  wcs_contains_point,
+)
+from datalab.datalab_session.utils.filecache import FileCache
+from datalab.datalab_session.utils.format import Format
+from datalab.datalab_session.utils.gaia import estimate_membership, gaia_cone_search
+
+log = logging.getLogger(__name__)
+log.setLevel(logging.INFO)
+
+
+class HRDiagram(BaseDataOperation):
+  # Fixed blue<->red match tolerance. Deliberately not a wizard input: every input is hashed
+  # into the cache key, so exposing it would fragment the cache and force full recomputes
+  MATCH_RADIUS_ARCSEC = 2.0
+  # Tighter tolerance for matching our stars to Gaia. Gaia DR3 positions are epoch 2016.0,
+  # so 1 arcsec also tolerates ~a decade of <100 mas/yr proper motion
+  GAIA_MATCH_RADIUS_ARCSEC = 1.0
+  # Keep only the brightest N matched stars to bound the output payload
+  MAX_OUTPUT_SOURCES = 5000
+  # Also emit up to this many of the brightest Gaia sources that did NOT match an image star
+  # (flagged gaia_only): the cluster's proper-motion clump and parallax peak stand out far
+  # better against the full Gaia field than against just the image-matched subset
+  MAX_GAIA_ONLY_SOURCES = 5000
+  DEFAULT_SEARCH_RADIUS_ARCMIN = 15.0
+  # Disjoint blue/red filter options so the wizard can only produce valid, wavelength-ordered
+  # (color = blue - red) pairs. BANZAI only reduces gp/rp/ip/zs, so gp is the only blue choice
+  BLUE_FILTER_OPTIONS = ['gp']
+  RED_FILTER_OPTIONS = ['rp', 'ip', 'zs']
+  # Gaia-only stars get CMD photometry from Gaia's synthetic SDSS-system magnitudes (see
+  # utils/gaia.py for the ~0.03 mag SDSS<->PS1 caveat baked into their errors)
+  GAIA_SYNTH_MAG = {'gp': 'g_sdss', 'rp': 'r_sdss', 'ip': 'i_sdss', 'zs': 'z_sdss'}
+  PROGRESS_STEPS = {
+    'BLUE_CATALOG_DONE': 0.2,
+    'RED_CATALOG_DONE': 0.4,
+    'CROSS_MATCH_DONE': 0.5,
+    'GAIA_QUERY_DONE': 0.80,
+    'GAIA_MATCH_DONE': 0.95,
+    'OUTPUT_DONE': 1.0,
+  }
+
+  @staticmethod
+  def name():
+    return 'HR Diagram'
+
+  @staticmethod
+  def description():
+    return """Builds a color-magnitude diagram (an observational HR diagram) of a star cluster from two images of it taken in different filters.
+
+The calibrated photometry catalogs of the two images are cross-matched by sky position, giving each star a color (blue - red magnitude) and a brightness. The output CMD can then be analyzed to estimate the cluster's distance, reddening, age, and metallicity by fitting isochrones."""
+
+  @staticmethod
+  def wizard_description():
+    return {
+      'name': HRDiagram.name(),
+      'description': HRDiagram.description(),
+      'category': 'image',
+      'inputs': {
+        'blue_filter_files': {
+          'name': 'Blue Band Image',
+          'description': 'The bluer filter image of the cluster',
+          'type': Format.FITS,
+          'single_filter': True,
+          'filter_options': HRDiagram.BLUE_FILTER_OPTIONS,
+          'minimum': 1,
+          'maximum': 1,
+        },
+        'red_filter_files': {
+          'name': 'Red Band Image',
+          'description': 'The redder filter image of the cluster (color = blue - red)',
+          'type': Format.FITS,
+          'single_filter': True,
+          'filter_options': HRDiagram.RED_FILTER_OPTIONS,
+          'minimum': 1,
+          'maximum': 1,
+        },
+        'cluster': {
+          'name': 'Cluster Center',
+          'description': 'The center of the star cluster to analyze',
+          'type': Format.SOURCE,
+        },
+        'search_radius_arcmin': {
+          'name': 'Search Radius (arcmin)',
+          'description': 'Include stars within this radius of the cluster center',
+          'type': Format.FLOAT,
+          'default': HRDiagram.DEFAULT_SEARCH_RADIUS_ARCMIN,
+        },
+      },
+    }
+
+  def _validate_cluster(self):
+    cluster = self.input_data.get('cluster') or {}
+    try:
+      cluster_ra = float(cluster.get('ra'))
+      cluster_dec = float(cluster.get('dec'))
+    except (TypeError, ValueError):
+      raise ClientAlertException(f'Operation {self.name()} requires a cluster center with ra and dec.')
+    return cluster.get('name', ''), cluster_ra, cluster_dec
+
+  def _validate_search_radius(self):
+    try:
+      search_radius_arcmin = float(self.input_data.get('search_radius_arcmin') or HRDiagram.DEFAULT_SEARCH_RADIUS_ARCMIN)
+    except (TypeError, ValueError):
+      raise ClientAlertException('The search radius must be a number of arcminutes.')
+    if search_radius_arcmin <= 0:
+      raise ClientAlertException('The search radius must be greater than zero arcminutes.')
+    return search_radius_arcmin
+
+  def _band_catalog(self, image: dict, user: User) -> dict:
+    """ Downloads one band's fits file and extracts its calibrated source catalog """
+    basename = image.get('basename')
+    try:
+      fits_path = FileCache().get_fits(basename, image.get('source', 'archive'), user)
+    except TimeoutError:
+      raise ClientAlertException(f'Download of {basename} timed out')
+
+    catalog = extract_calibrated_catalog(fits_path, basename=basename)
+    if len(catalog['ra']) == 0:
+      raise ClientAlertException(f'No calibrated sources found in the catalog of {basename}.')
+    catalog['fits_path'] = fits_path
+    return catalog
+
+  def operate(self, submitter: User):
+    blue_image = self._validate_inputs(input_key='blue_filter_files')[0]
+    red_image = self._validate_inputs(input_key='red_filter_files')[0]
+    blue_filter = blue_image.get('filter', blue_image.get('primary_optical_element', '')) or ''
+    red_filter = red_image.get('filter', red_image.get('primary_optical_element', '')) or ''
+    cluster_name, cluster_ra, cluster_dec = self._validate_cluster()
+    search_radius_arcmin = self._validate_search_radius()
+
+    log.info(f'HR Diagram for cluster ({cluster_ra}, {cluster_dec}) from images '
+             f'{blue_image["basename"]} ({blue_filter}) and {red_image["basename"]} ({red_filter})')
+
+    blue_catalog = self._band_catalog(blue_image, submitter)
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['BLUE_CATALOG_DONE'])
+    red_catalog = self._band_catalog(red_image, submitter)
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['RED_CATALOG_DONE'])
+
+    # cheap pre-flight to catch mistyped coordinates before cross-matching
+    if not wcs_contains_point(blue_catalog['fits_path'], cluster_ra, cluster_dec) \
+        and not wcs_contains_point(red_catalog['fits_path'], cluster_ra, cluster_dec):
+      raise ClientAlertException('The cluster center is outside both image footprints - '
+                                 'check that the cluster coordinates match your images.')
+
+    blue_indices, red_indices = cross_match_one_to_one(blue_catalog, red_catalog, HRDiagram.MATCH_RADIUS_ARCSEC)
+    if len(blue_indices) == 0:
+      raise ClientAlertException('No stars matched between the two bands - '
+                                 'check that both images cover the same field.')
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['CROSS_MATCH_DONE'])
+
+    # the red (y-axis) band positions are the canonical star positions
+    ra = red_catalog['ra'][red_indices]
+    dec = red_catalog['dec'][red_indices]
+    blue_mag = blue_catalog['mag'][blue_indices]
+    blue_magerr = blue_catalog['magerr'][blue_indices]
+    red_mag = red_catalog['mag'][red_indices]
+    red_magerr = red_catalog['magerr'][red_indices]
+
+    in_cone = cone_filter(ra, dec, cluster_ra, cluster_dec, search_radius_arcmin)
+    if not in_cone.any():
+      raise ClientAlertException(f'No matched stars within {search_radius_arcmin} arcminutes of the '
+                                 'cluster center - try a larger search radius.')
+    n_stars_matched = int(in_cone.sum())
+
+    # keep the brightest stars first when capping the output size
+    brightest = np.argsort(red_mag[in_cone])[:HRDiagram.MAX_OUTPUT_SOURCES]
+    ra, dec = ra[in_cone][brightest], dec[in_cone][brightest]
+    blue_mag, blue_magerr = blue_mag[in_cone][brightest], blue_magerr[in_cone][brightest]
+    red_mag, red_magerr = red_mag[in_cone][brightest], red_magerr[in_cone][brightest]
+
+    # Gaia enrichment: proper motions + parallaxes for cluster membership selection.
+    # A Gaia failure raises and fails the operation (re-runnable) rather than silently
+    # caching a membership-less result for 30 days
+    gaia_data = gaia_cone_search(cluster_ra, cluster_dec, search_radius_arcmin)
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['GAIA_QUERY_DONE'])
+    star_indices, gaia_indices = cross_match_one_to_one({'ra': ra, 'dec': dec}, gaia_data,
+                                                        HRDiagram.GAIA_MATCH_RADIUS_ARCSEC)
+    gaia_for_star = {int(star): int(gaia) for star, gaia in zip(star_indices, gaia_indices)}
+
+    # the brightest Gaia sources without an image counterpart still map the cluster's
+    # kinematics, so they are emitted too (flagged gaia_only) for the membership plots
+    matched_gaia = np.asarray(gaia_indices, dtype=int)
+    unmatched_gaia = np.setdiff1d(np.arange(len(gaia_data['ra'])), matched_gaia)
+    unmatched_g_mag = gaia_data['phot_g_mean_mag'][unmatched_gaia]
+    by_brightness = np.argsort(np.where(np.isfinite(unmatched_g_mag), unmatched_g_mag, np.inf))
+    gaia_only_indices = unmatched_gaia[by_brightness][:HRDiagram.MAX_GAIA_ONLY_SOURCES]
+
+    # the membership guess uses the same star set the membership plots will show
+    membership_pool = np.concatenate([matched_gaia, gaia_only_indices])
+    membership_guess = estimate_membership(
+      gaia_data['pmra'][membership_pool],
+      gaia_data['pmdec'][membership_pool],
+      gaia_data['parallax'][membership_pool],
+      gaia_data['r_med_geo'][membership_pool],
+      gaia_data['r_lo_geo'][membership_pool],
+      gaia_data['r_hi_geo'][membership_pool],
+    )
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['GAIA_MATCH_DONE'])
+
+    def gaia_value(column, gaia_index):
+      # non-finite (masked) Gaia values become None to stay JSON-safe
+      value = float(gaia_data[column][gaia_index])
+      return round(value, 4) if np.isfinite(value) else None
+
+    def gaia_astrometry(gaia_index):
+      # proper motion, parallax, Bailer-Jones geometric distance, and the gaia_match flag for a
+      # star (r_med_geo estimate + r_lo_geo/r_hi_geo 16th/84th percentile bounds.
+      # The astrometry fields are None when the star has no match.
+      matched = gaia_index is not None
+      def value(column):
+        return gaia_value(column, gaia_index) if matched else None
+      return {
+        'pmra': value('pmra'),
+        'pmra_err': value('pmra_error'),
+        'pmdec': value('pmdec'),
+        'pmdec_err': value('pmdec_error'),
+        'parallax': value('parallax'),
+        'parallax_err': value('parallax_error'),
+        'distance': value('r_med_geo'),
+        'distance_lo': value('r_lo_geo'),
+        'distance_hi': value('r_hi_geo'),
+        'gaia_match': matched,
+      }
+
+    cmd_points = []
+    # Iterate over points found in our input images and fill in gaia extra details
+    for i in range(len(ra)):
+      point = {
+        'ra': round(float(ra[i]), 6),
+        'dec': round(float(dec[i]), 6),
+        'color': round(float(blue_mag[i] - red_mag[i]), 4),
+        'color_err': round(float(np.sqrt(blue_magerr[i] ** 2 + red_magerr[i] ** 2)), 4),
+        'mag': round(float(red_mag[i]), 4),
+        'magerr': round(float(red_magerr[i]), 4),
+      }
+      point.update(gaia_astrometry(gaia_for_star.get(i)))
+      point['gaia_only'] = False
+      cmd_points.append(point)
+
+    n_image_stars = len(cmd_points)
+    # Gaia-only stars: no image detection, but their astrometry feeds the membership plots
+    # and Gaia's synthetic photometry (where the source has it) also places them on the CMD
+    blue_synth = HRDiagram.GAIA_SYNTH_MAG[blue_filter]
+    red_synth = HRDiagram.GAIA_SYNTH_MAG[red_filter]
+    for gaia_index in gaia_only_indices:
+      # synthetic blue/red mags only feed color and the y-axis mag; they are not emitted per-star
+      blue_synth_mag = gaia_value(f'{blue_synth}_mag', gaia_index)
+      red_synth_mag = gaia_value(f'{red_synth}_mag', gaia_index)
+      blue_err = gaia_value(f'{blue_synth}_mag_error', gaia_index)
+      red_err = gaia_value(f'{red_synth}_mag_error', gaia_index)
+      point = {
+        'ra': round(float(gaia_data['ra'][gaia_index]), 6),
+        'dec': round(float(gaia_data['dec'][gaia_index]), 6),
+        'g_mag': gaia_value('phot_g_mean_mag', gaia_index),
+        'mag': red_synth_mag,
+        'magerr': red_err,
+        'gaia_only': True,
+      }
+      point.update(gaia_astrometry(gaia_index))
+      if blue_synth_mag is not None and red_synth_mag is not None:
+        point['color'] = round(blue_synth_mag - red_synth_mag, 4)
+        point['color_err'] = round(float(np.hypot(blue_err, red_err)), 4) \
+          if blue_err is not None and red_err is not None else None
+      else:
+        point['color'] = None
+        point['color_err'] = None
+      cmd_points.append(point)
+
+    output = {
+      'output_data': [
+        {
+          'cluster': {'name': cluster_name, 'ra': cluster_ra, 'dec': cluster_dec, 'radius_arcmin': search_radius_arcmin},
+          'blue_filter': blue_filter,
+          'red_filter': red_filter,
+          'mag_band': red_filter,
+          'cmd': cmd_points,
+          'membership_guess': membership_guess,
+          'n_gaia_matched': len(star_indices),
+          'n_gaia_only': len(gaia_only_indices),
+          'n_stars': n_image_stars,
+          'n_stars_matched': n_stars_matched,
+        }
+      ]
+    }
+    log.info(f'HR Diagram output {n_image_stars} image stars from {len(blue_indices)} band matches, '
+             f'{len(star_indices)} Gaia matched, plus {len(gaia_only_indices)} Gaia-only stars')
+
+    self.set_output(output, is_raw=True)
+    self.set_operation_progress(HRDiagram.PROGRESS_STEPS['OUTPUT_DONE'])
+    self.set_status('COMPLETED')
diff --git a/datalab/datalab_session/tests/test_operations.py b/datalab/datalab_session/tests/test_operations.py
index bc1f338..b816033 100644
--- a/datalab/datalab_session/tests/test_operations.py
+++ b/datalab/datalab_session/tests/test_operations.py
@@ -7,12 +7,14 @@
 import os
 
 from astropy.io import fits
+from astropy.wcs import WCS
 import numpy as np
 
 from datalab.datalab_session.data_operations.data_operation import BaseDataOperation
 from datalab.datalab_session.data_operations.aperture_photometry import AperturePhotometry
 from datalab.datalab_session.data_operations.color_image import Color_Image
 from datalab.datalab_session.exceptions import ClientAlertException
+from datalab.datalab_session.data_operations.hr_diagram import HRDiagram
 from datalab.datalab_session.data_operations.light_curve import LightCurve
 from datalab.datalab_session.data_operations.median import Median
 from datalab.datalab_session.data_operations.stacking import Stack
@@ -450,6 +452,353 @@ def test_operate(self, mock_file_cache, mock_named_tempfile, mock_create_jpgs, m
         self.assertEqual(output, [self.temp_color_path])
 
 
+class TestHRDiagramOperation(FileExtendedTestCase):
+    temp_blue_path = f'{test_path}temp_hr_blue.fits'
+    temp_red_path = f'{test_path}temp_hr_red.fits'
+
+    CENTER_RA = 150.0
+    CENTER_DEC = 30.0
+
+    def tearDown(self):
+        self.clean_test_dir()
+        return super().tearDown()
+
+    @staticmethod
+    def create_test_fits(path, center_ra, center_dec, ra, dec, mag, magerr, include_radec=True, include_mag=True):
+        """ Writes a fits file with a WCS'd SCI HDU and a CAT HDU like a reduced image's catalog """
+        wcs = WCS(naxis=2)
+        wcs.wcs.ctype = ['RA---TAN', 'DEC--TAN']
+        wcs.wcs.crval = [center_ra, center_dec]
+        wcs.wcs.crpix = [100.0, 100.0]
+        wcs.wcs.cdelt = [-1.0 / 3600.0, 1.0 / 3600.0]
+        sci_hdu = fits.ImageHDU(data=np.zeros((200, 200), dtype=np.float32), header=wcs.to_header(), name='SCI')
+
+        x, y = wcs.all_world2pix(np.asarray(ra), np.asarray(dec), 1)
+        columns = [
+            fits.Column(name='x', format='D', array=x),
+            fits.Column(name='y', format='D', array=y),
+        ]
+        if include_radec:
+            columns += [
+                fits.Column(name='ra', format='D', array=np.asarray(ra)),
+                fits.Column(name='dec', format='D', array=np.asarray(dec)),
+            ]
+        if include_mag:
+            columns += [
+                fits.Column(name='mag', format='D', array=np.asarray(mag)),
+                fits.Column(name='magerr', format='D', array=np.asarray(magerr)),
+            ]
+        cat_hdu = fits.BinTableHDU.from_columns(columns, name='CAT')
+        fits.HDUList([fits.PrimaryHDU(), sci_hdu, cat_hdu]).writeto(path, overwrite=True)
+
+    def hr_input_data(self, search_radius_arcmin=None):
+        input_data = {
+            'blue_filter_files': [{'basename': 'blue_fits', 'source': 'local', 'filter': 'gp'}],
+            'red_filter_files': [{'basename': 'red_fits', 'source': 'local', 'filter': 'rp'}],
+            'cluster': {'name': 'Test Cluster', 'ra': self.CENTER_RA, 'dec': self.CENTER_DEC},
+        }
+        if search_radius_arcmin is not None:
+            input_data['search_radius_arcmin'] = search_radius_arcmin
+        return input_data
+
+    def mock_band_fits(self, mock_file_cache):
+        """ FileCache is instantiated once per band, blue first """
+        mock_fc_blue = mock.MagicMock()
+        mock_fc_blue.get_fits.return_value = self.temp_blue_path
+        mock_fc_red = mock.MagicMock()
+        mock_fc_red.get_fits.return_value = self.temp_red_path
+        mock_file_cache.side_effect = [mock_fc_blue, mock_fc_red]
+
+    @classmethod
+    def empty_gaia_data(cls):
+        return cls.gaia_field(ra=[], dec=[], pmra=[], pmdec=[], parallax=[])
+
+    @staticmethod
+    def gaia_field(ra, dec, pmra, pmdec, parallax, g_mag=None, synth_g=None, synth_r=None, distance=None):
+        """ Builds a gaia_cone_search-shaped dict; synthetic i/z bands mirror r, and the
+        Bailer-Jones distance bounds bracket r_med_geo asymmetrically """
+        n = len(ra)
+        data = {
+            'ra': np.asarray(ra, dtype=float),
+            'dec': np.asarray(dec, dtype=float),
+            'pmra': np.asarray(pmra, dtype=float),
+            'pmra_error': np.full(n, 0.05),
+            'pmdec': np.asarray(pmdec, dtype=float),
+            'pmdec_error': np.full(n, 0.06),
+            'parallax': np.asarray(parallax, dtype=float),
+            'parallax_error': np.full(n, 0.04),
+            'phot_g_mean_mag': np.full(n, 14.0) if g_mag is None else np.asarray(g_mag, dtype=float),
+        }
+        distance = np.full(n, 800.0) if distance is None else np.asarray(distance, dtype=float)
+        data['r_med_geo'] = distance
+        data['r_lo_geo'] = distance - 50.0
+        data['r_hi_geo'] = distance + 60.0
+        synth_g = np.full(n, 15.0) if synth_g is None else np.asarray(synth_g, dtype=float)
+        synth_r = np.full(n, 14.4) if synth_r is None else np.asarray(synth_r, dtype=float)
+        for band, mags in [('g_sdss', synth_g), ('r_sdss', synth_r), ('i_sdss', synth_r), ('z_sdss', synth_r)]:
+            data[f'{band}_mag'] = mags
+            data[f'{band}_mag_error'] = np.where(np.isfinite(mags), 0.032, np.nan)
+        return data
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.gaia_cone_search')
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_operate(self, mock_file_cache, mock_gaia_cone_search):
+        # five cluster stars present in both bands, at 10-50 arcsec north of the center
+        common_dec = self.CENTER_DEC + np.array([10.0, 20.0, 30.0, 40.0, 50.0]) / 3600.0
+        common_ra = np.full(5, self.CENTER_RA)
+        blue_common_mag = np.array([15.0, 15.5, 16.0, 16.5, 17.0])
+        red_common_mag = np.array([14.5, 14.8, 15.2, 15.6, 16.0])
+
+        # a sixth red star 10 arcsec south with two blue sources nearby (0 and 1 arcsec away),
+        # to check one-to-one dedup keeps only the closest pair
+        crowded_dec = self.CENTER_DEC - 10.0 / 3600.0
+        # plus per-band strays with no counterpart within the match radius
+        blue_ra = np.concatenate([common_ra, [self.CENTER_RA, self.CENTER_RA, self.CENTER_RA]])
+        blue_dec = np.concatenate([common_dec, [crowded_dec, crowded_dec + 1.0 / 3600.0, self.CENTER_DEC + 80.0 / 3600.0]])
+        blue_mag = np.concatenate([blue_common_mag, [18.0, 19.0, 15.5]])
+        red_ra = np.concatenate([common_ra, [self.CENTER_RA, self.CENTER_RA]])
+        red_dec = np.concatenate([common_dec, [crowded_dec, self.CENTER_DEC - 40.0 / 3600.0]])
+        red_mag = np.concatenate([red_common_mag, [17.0, 15.0]])
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC,
+                              blue_ra, blue_dec, blue_mag, np.full(len(blue_mag), 0.02))
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC,
+                              red_ra, red_dec, red_mag, np.full(len(red_mag), 0.03))
+        self.mock_band_fits(mock_file_cache)
+
+        # Gaia knows the four brightest cluster stars, plus a stray with no photometric counterpart
+        gaia_dec = np.concatenate([common_dec[:4], [self.CENTER_DEC - 60.0 / 3600.0]])
+        mock_gaia_cone_search.return_value = self.gaia_field(
+            ra=np.full(5, self.CENTER_RA),
+            dec=gaia_dec,
+            pmra=[-5.1, -4.9, -5.0, -5.05, 12.0],
+            pmdec=[3.0, 3.1, 2.9, 3.02, -8.0],
+            parallax=[1.2, 1.3, 1.25, 1.22, 0.1],
+            synth_g=[15.0, 15.5, 16.0, 16.5, 16.2],
+            synth_r=[14.5, 14.8, 15.2, 15.6, 15.4],
+            distance=[810.0, 820.0, 830.0, 840.0, np.nan],
+        )
+
+        hr_diagram = HRDiagram(self.hr_input_data())
+        hr_diagram.operate(None)
+        output = hr_diagram.get_output()['output_data'][0]
+
+        self.assertEqual(hr_diagram.get_status(), 'COMPLETED')
+        self.assertEqual(hr_diagram.get_operation_progress(), 1.0)
+        self.assertEqual(output['n_stars'], 6)
+        self.assertEqual(output['n_stars_matched'], 6)
+        self.assertEqual(output['blue_filter'], 'gp')
+        self.assertEqual(output['red_filter'], 'rp')
+        self.assertEqual(output['mag_band'], 'rp')
+        self.assertEqual(output['cluster']['name'], 'Test Cluster')
+        mock_gaia_cone_search.assert_called_once_with(self.CENTER_RA, self.CENTER_DEC, 15.0)
+
+        # cmd points are sorted brightest first in the red (y-axis) band
+        expected_mags = [14.5, 14.8, 15.2, 15.6, 16.0, 17.0]
+        expected_colors = [0.5, 0.7, 0.8, 0.9, 1.0, 1.0]
+        for point, expected_mag, expected_color in zip(output['cmd'], expected_mags, expected_colors):
+            self.assertAlmostEqual(point['mag'], expected_mag, places=3)
+            self.assertAlmostEqual(point['color'], expected_color, places=3)
+            self.assertAlmostEqual(point['color_err'], np.sqrt(0.02 ** 2 + 0.03 ** 2), places=3)
+
+        # the four brightest stars picked up Gaia proper motions and parallaxes, the rest did not
+        self.assertEqual(output['n_gaia_matched'], 4)
+        self.assertEqual([point['gaia_match'] for point in output['cmd'][:6]], [True] * 4 + [False] * 2)
+        self.assertAlmostEqual(output['cmd'][0]['pmra'], -5.1)
+        self.assertAlmostEqual(output['cmd'][0]['pmdec'], 3.0)
+        self.assertAlmostEqual(output['cmd'][0]['parallax'], 1.2)
+        self.assertIsNone(output['cmd'][4]['pmra'])
+        self.assertIsNone(output['cmd'][5]['parallax'])
+        # Bailer-Jones geometric distance rides along with each Gaia match, bracketed by its bounds
+        self.assertAlmostEqual(output['cmd'][0]['distance'], 810.0)
+        self.assertAlmostEqual(output['cmd'][0]['distance_lo'], 760.0)
+        self.assertAlmostEqual(output['cmd'][0]['distance_hi'], 870.0)
+        # an unmatched image star has no Gaia distance
+        self.assertIsNone(output['cmd'][5]['distance'])
+        # five pool stars is below the clump threshold, so no automatic membership guess
+        self.assertIsNone(output['membership_guess'])
+
+        # the unmatched Gaia stray is appended as a gaia_only star with synthetic photometry
+        self.assertEqual(output['n_gaia_only'], 1)
+        self.assertEqual(len(output['cmd']), 7)
+        self.assertEqual([point['gaia_only'] for point in output['cmd']], [False] * 6 + [True])
+        gaia_only_point = output['cmd'][6]
+        self.assertAlmostEqual(gaia_only_point['pmra'], 12.0)
+        self.assertAlmostEqual(gaia_only_point['g_mag'], 14.0)
+        self.assertAlmostEqual(gaia_only_point['color'], 0.8, places=3)
+        self.assertAlmostEqual(gaia_only_point['mag'], 15.4, places=3)
+        # this stray has no Bailer-Jones entry (nan distance), so its distance is null while its pm rides along
+        self.assertIsNone(gaia_only_point['distance'])
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.gaia_cone_search')
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_gaia_only_stars_capped_and_brightest_first(self, mock_file_cache, mock_gaia_cone_search):
+        # two image stars near the center with no Gaia counterparts
+        star_dec = self.CENTER_DEC + np.array([10.0, 20.0]) / 3600.0
+        star_ra = np.full(2, self.CENTER_RA)
+        red_mags = np.array([15.0, 16.0])
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC,
+                              star_ra, star_dec, red_mags + 0.5, np.full(2, 0.02))
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC,
+                              star_ra, star_dec, red_mags, np.full(2, 0.03))
+        self.mock_band_fits(mock_file_cache)
+
+        # five unmatched Gaia field stars: G mags shuffled, one with no G mag at all,
+        # and the brightest one missing its synthetic photometry
+        gaia_dec = self.CENTER_DEC - np.array([60.0, 70.0, 80.0, 90.0, 100.0]) / 3600.0
+        mock_gaia_cone_search.return_value = self.gaia_field(
+            ra=np.full(5, self.CENTER_RA),
+            dec=gaia_dec,
+            pmra=np.full(5, 4.0), pmdec=np.full(5, -2.0), parallax=np.full(5, 0.8),
+            g_mag=[15.0, 11.0, 13.0, np.nan, 12.0],
+            synth_g=[15.8, np.nan, 13.8, 14.1, 12.8],
+            synth_r=[15.1, np.nan, 13.1, 13.5, 12.1],
+        )
+
+        with mock.patch.object(HRDiagram, 'MAX_GAIA_ONLY_SOURCES', 3):
+            hr_diagram = HRDiagram(self.hr_input_data())
+            hr_diagram.operate(None)
+        output = hr_diagram.get_output()['output_data'][0]
+
+        self.assertEqual(output['n_stars'], 2)
+        self.assertEqual(output['n_gaia_matched'], 0)
+        self.assertEqual(output['n_gaia_only'], 3)
+        gaia_only_points = output['cmd'][2:]
+        # the cap keeps the brightest three by G mag, ascending; the source with no G mag lost out
+        self.assertEqual([point['g_mag'] for point in gaia_only_points], [11.0, 12.0, 13.0])
+        self.assertTrue(all(point['gaia_only'] for point in gaia_only_points))
+        # no synthetic photometry -> the star appears on the membership plots but not the CMD
+        self.assertIsNone(gaia_only_points[0]['color'])
+        self.assertIsNone(gaia_only_points[0]['mag'])
+        self.assertAlmostEqual(gaia_only_points[1]['color'], 0.7, places=3)
+        self.assertAlmostEqual(gaia_only_points[1]['mag'], 12.1, places=3)
+        # Bailer-Jones distance rides along even for gaia-only stars
+        self.assertAlmostEqual(gaia_only_points[1]['distance'], 800.0)
+        self.assertAlmostEqual(gaia_only_points[1]['distance_lo'], 750.0)
+        self.assertAlmostEqual(gaia_only_points[1]['distance_hi'], 860.0)
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.gaia_cone_search')
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_cone_filter_excludes_far_stars(self, mock_file_cache, mock_gaia_cone_search):
+        # two stars near the center and one 3 arcmin away, with a 1 arcmin search radius
+        ra = np.full(3, self.CENTER_RA)
+        dec = self.CENTER_DEC + np.array([10.0, 20.0, 180.0]) / 3600.0
+        mag = np.array([15.0, 16.0, 17.0])
+        magerr = np.full(3, 0.02)
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag + 0.5, magerr)
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr)
+        self.mock_band_fits(mock_file_cache)
+        mock_gaia_cone_search.return_value = self.empty_gaia_data()
+
+        hr_diagram = HRDiagram(self.hr_input_data(search_radius_arcmin=1.0))
+        hr_diagram.operate(None)
+        output = hr_diagram.get_output()['output_data'][0]
+
+        self.assertEqual(output['n_stars'], 2)
+        self.assertEqual(output['cluster']['radius_arcmin'], 1.0)
+        # an empty Gaia field is not an error, the stars are just unmatched
+        self.assertEqual(output['n_gaia_matched'], 0)
+        self.assertIsNone(output['membership_guess'])
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.gaia_cone_search')
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_radec_from_wcs_fallback(self, mock_file_cache, mock_gaia_cone_search):
+        # the blue catalog has no ra/dec columns, so they are computed from its x/y and WCS
+        ra = np.full(3, self.CENTER_RA)
+        dec = self.CENTER_DEC + np.array([10.0, 20.0, 30.0]) / 3600.0
+        mag = np.array([15.0, 16.0, 17.0])
+        magerr = np.full(3, 0.02)
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag + 0.5, magerr,
+                              include_radec=False)
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr)
+        self.mock_band_fits(mock_file_cache)
+        mock_gaia_cone_search.return_value = self.empty_gaia_data()
+
+        hr_diagram = HRDiagram(self.hr_input_data())
+        hr_diagram.operate(None)
+        output = hr_diagram.get_output()['output_data'][0]
+
+        self.assertEqual(hr_diagram.get_status(), 'COMPLETED')
+        self.assertEqual(output['n_stars'], 3)
+        self.assertAlmostEqual(output['cmd'][0]['ra'], self.CENTER_RA, places=5)
+        self.assertAlmostEqual(output['cmd'][0]['color'], 0.5, places=3)
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.gaia_cone_search')
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_gaia_failure_fails_operation(self, mock_file_cache, mock_gaia_cone_search):
+        # a Gaia outage fails the operation (re-runnable) instead of caching a degraded result
+        ra = np.full(2, self.CENTER_RA)
+        dec = self.CENTER_DEC + np.array([10.0, 20.0]) / 3600.0
+        mag = np.array([15.0, 16.0])
+        magerr = np.full(2, 0.02)
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag + 0.5, magerr)
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr)
+        self.mock_band_fits(mock_file_cache)
+        mock_gaia_cone_search.side_effect = ClientAlertException('Could not fetch Gaia data for the cluster field')
+
+        hr_diagram = HRDiagram(self.hr_input_data())
+        with self.assertRaisesRegex(ClientAlertException, 'Gaia'):
+            hr_diagram.operate(None)
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_uncalibrated_band_fails(self, mock_file_cache):
+        ra = np.full(2, self.CENTER_RA)
+        dec = self.CENTER_DEC + np.array([10.0, 20.0]) / 3600.0
+        mag = np.array([15.0, 16.0])
+        magerr = np.full(2, 0.02)
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr,
+                              include_mag=False)
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr)
+        self.mock_band_fits(mock_file_cache)
+
+        hr_diagram = HRDiagram(self.hr_input_data())
+        with self.assertRaisesRegex(ClientAlertException, 'zero-point calibrated'):
+            hr_diagram.operate(None)
+
+    @mock.patch('datalab.datalab_session.data_operations.hr_diagram.FileCache')
+    def test_disjoint_fields_fail(self, mock_file_cache):
+        # the red image is a degree away, so no stars cross-match
+        ra = np.full(2, self.CENTER_RA)
+        dec = self.CENTER_DEC + np.array([10.0, 20.0]) / 3600.0
+        mag = np.array([15.0, 16.0])
+        magerr = np.full(2, 0.02)
+
+        self.create_test_fits(self.temp_blue_path, self.CENTER_RA, self.CENTER_DEC, ra, dec, mag, magerr)
+        self.create_test_fits(self.temp_red_path, self.CENTER_RA + 1.0, self.CENTER_DEC,
+                              ra + 1.0, dec, mag, magerr)
+        self.mock_band_fits(mock_file_cache)
+
+        hr_diagram = HRDiagram(self.hr_input_data())
+        with self.assertRaisesRegex(ClientAlertException, 'No stars matched'):
+            hr_diagram.operate(None)
+
+    def test_missing_cluster_fails(self):
+        input_data = self.hr_input_data()
+        del input_data['cluster']
+        hr_diagram = HRDiagram(input_data)
+
+        with self.assertRaisesRegex(ClientAlertException, 'cluster center'):
+            hr_diagram.operate(None)
+
+    def test_bad_search_radius_fails(self):
+        hr_diagram = HRDiagram(self.hr_input_data(search_radius_arcmin=-5.0))
+
+        with self.assertRaisesRegex(ClientAlertException, 'search radius'):
+            hr_diagram.operate(None)
+
+    def test_missing_input_files_fails(self):
+        input_data = self.hr_input_data()
+        input_data['red_filter_files'] = []
+        hr_diagram = HRDiagram(input_data)
+
+        with self.assertRaises(ClientAlertException):
+            hr_diagram.operate(None)
+
+
 class TestStackOperation(FileExtendedTestCase):
     test_fits_1_path = f'{test_path}fits_1.fits.fz'
     test_fits_2_path = f'{test_path}fits_2.fits.fz'
diff --git a/datalab/datalab_session/tests/test_utils.py b/datalab/datalab_session/tests/test_utils.py
index a56a156..f2c4b9f 100644
--- a/datalab/datalab_session/tests/test_utils.py
+++ b/datalab/datalab_session/tests/test_utils.py
@@ -1,9 +1,16 @@
+from unittest import mock
+
+from astropy.table import Table, MaskedColumn
+
 from datalab.datalab_session.utils.file_utils import *
 from datalab.datalab_session.utils.s3_utils import *
 from datalab.datalab_session.utils.flux_to_mag import flux_to_mag, flux_to_mag_array, flux_to_mag_scalar
 from datalab.datalab_session.utils.geometry import angular_distance_arcsec, distance_pixels
 from datalab.datalab_session.utils.centroiding import BackgroundModel, _fit_plane
 from datalab.datalab_session.utils.photometry import fractional_pixel_overlap, measure_aperture
+from datalab.datalab_session.utils.catalog_utils import cross_match_one_to_one, cone_filter
+from datalab.datalab_session.utils.gaia import GAIA_PARALLAX_ZERO_POINT_MAS, estimate_membership, gaia_cone_search
+from datalab.datalab_session.exceptions import ClientAlertException
 from datalab.datalab_session.tests.test_files.file_extended_test_case import FileExtendedTestCase
 
 class FileUtilsTestClass(FileExtendedTestCase):
@@ -167,3 +174,185 @@ def test_fit_plane_rejects_degenerate_points(self):
     ]
 
     self.assertIsNone(_fit_plane(points))
+
+class CatalogUtilsTestClass(FileExtendedTestCase):
+
+  @staticmethod
+  def catalog(dec_offsets_arcsec, ra=150.0, dec=30.0):
+    """ Builds a minimal catalog dict of points offset north of (ra, dec) by arcseconds """
+    return {
+      'ra': np.full(len(dec_offsets_arcsec), ra),
+      'dec': dec + np.array(dec_offsets_arcsec) / 3600.0,
+    }
+
+  def test_cross_match_matches_within_radius(self):
+    catalog_a = self.catalog([0.0, 10.0])
+    catalog_b = self.catalog([0.5, 10.0])
+
+    a_indices, b_indices = cross_match_one_to_one(catalog_a, catalog_b, 2.0)
+
+    self.assertEqual(a_indices.tolist(), [0, 1])
+    self.assertEqual(b_indices.tolist(), [0, 1])
+
+  def test_cross_match_applies_separation_cut(self):
+    catalog_a = self.catalog([0.0])
+    catalog_b = self.catalog([3.0])  # nearest neighbor is 3 arcsec away
+
+    a_indices, b_indices = cross_match_one_to_one(catalog_a, catalog_b, 2.0)
+
+    self.assertEqual(len(a_indices), 0)
+    self.assertEqual(len(b_indices), 0)
+
+  def test_cross_match_dedups_to_closest_pair(self):
+    # two a sources share the same nearest b source, only the closer pair survives
+    catalog_a = self.catalog([0.0, 1.0])
+    catalog_b = self.catalog([0.0])
+
+    a_indices, b_indices = cross_match_one_to_one(catalog_a, catalog_b, 2.0)
+
+    self.assertEqual(a_indices.tolist(), [0])
+    self.assertEqual(b_indices.tolist(), [0])
+
+  def test_cross_match_empty_catalogs(self):
+    empty = {'ra': np.array([]), 'dec': np.array([])}
+    catalog = self.catalog([0.0])
+
+    for catalog_a, catalog_b in [(empty, catalog), (catalog, empty), (empty, empty)]:
+      a_indices, b_indices = cross_match_one_to_one(catalog_a, catalog_b, 2.0)
+      self.assertEqual(len(a_indices), 0)
+      self.assertEqual(len(b_indices), 0)
+
+  def test_cone_filter(self):
+    catalog = self.catalog([30.0, 120.0])  # 0.5 and 2 arcmin north of center
+
+    in_cone = cone_filter(catalog['ra'], catalog['dec'], 150.0, 30.0, 1.0)
+
+    self.assertEqual(in_cone.tolist(), [True, False])
+
+
+class GaiaUtilsTestClass(FileExtendedTestCase):
+
+  @staticmethod
+  def gaia_table():
+    """ A fake Gaia archive result: the second source is missing its astrometric solution,
+    its Bailer-Jones distance, and (like any source without published BP/RP spectra) its
+    synthetic photometry """
+    columns = {
+      'ra': [150.0, 150.001],
+      'dec': [30.0, 30.001],
+      'pmra': MaskedColumn([5.0, 0.0], mask=[False, True]),
+      'pmra_error': MaskedColumn([0.05, 0.0], mask=[False, True]),
+      'pmdec': MaskedColumn([-3.0, 0.0], mask=[False, True]),
+      'pmdec_error': MaskedColumn([0.06, 0.0], mask=[False, True]),
+      'parallax': MaskedColumn([1.25, 0.0], mask=[False, True]),
+      'parallax_error': MaskedColumn([0.04, 0.0], mask=[False, True]),
+      'phot_g_mean_mag': [14.2, 17.9],
+      'r_med_geo': MaskedColumn([800.0, 0.0], mask=[False, True]),
+      'r_lo_geo': MaskedColumn([760.0, 0.0], mask=[False, True]),
+      'r_hi_geo': MaskedColumn([845.0, 0.0], mask=[False, True]),
+    }
+    for band in ['g_sdss', 'r_sdss', 'i_sdss', 'z_sdss']:
+      columns[f'{band}_mag'] = MaskedColumn([15.0, 0.0], mask=[False, True])
+      columns[f'{band}_flux'] = MaskedColumn([1000.0, 0.0], mask=[False, True])
+      columns[f'{band}_flux_error'] = MaskedColumn([10.0, 0.0], mask=[False, True])
+    return Table(columns)
+
+  @mock.patch('astroquery.gaia.Gaia')
+  def test_gaia_cone_search_parses_and_caches(self, mock_gaia):
+    mock_gaia.launch_job_async.return_value.get_results.return_value = self.gaia_table()
+
+    # unique field so this test never hits another test's cache entry
+    gaia_data = gaia_cone_search(210.1234, -5.5678, 12.0)
+
+    self.assertEqual(len(gaia_data['ra']), 2)
+    self.assertAlmostEqual(gaia_data['pmra'][0], 5.0)
+    self.assertAlmostEqual(gaia_data['parallax'][0], 1.25 + GAIA_PARALLAX_ZERO_POINT_MAS)
+    # masked values come back as nan
+    self.assertTrue(np.isnan(gaia_data['pmra'][1]))
+    self.assertTrue(np.isnan(gaia_data['parallax'][1]))
+    # Bailer-Jones geometric distance parses; the source without an entry comes back nan
+    self.assertAlmostEqual(gaia_data['r_med_geo'][0], 800.0)
+    self.assertAlmostEqual(gaia_data['r_lo_geo'][0], 760.0)
+    self.assertAlmostEqual(gaia_data['r_hi_geo'][0], 845.0)
+    self.assertTrue(np.isnan(gaia_data['r_med_geo'][1]))
+    # synthetic photometry parses, with flux errors propagated onto the systematic floor
+    self.assertAlmostEqual(gaia_data['g_sdss_mag'][0], 15.0)
+    expected_error = np.sqrt((2.5 / np.log(10) * 10.0 / 1000.0) ** 2 + 0.03 ** 2)
+    self.assertAlmostEqual(gaia_data['g_sdss_mag_error'][0], expected_error, places=5)
+    self.assertTrue(np.isnan(gaia_data['g_sdss_mag'][1]))
+    self.assertTrue(np.isnan(gaia_data['g_sdss_mag_error'][1]))
+
+    # a second identical search is served from the cache without re-querying the archive
+    gaia_cone_search(210.1234, -5.5678, 12.0)
+    mock_gaia.launch_job_async.assert_called_once()
+
+  @mock.patch('astroquery.gaia.Gaia')
+  def test_gaia_cone_search_failure_raises(self, mock_gaia):
+    mock_gaia.launch_job_async.side_effect = ConnectionError('archive down')
+
+    with self.assertRaisesRegex(ClientAlertException, 'Gaia'):
+      gaia_cone_search(33.3333, 44.4444, 10.0)
+
+  def test_estimate_membership_finds_clump(self):
+    rng = np.random.default_rng(42)
+    # a 40 star cluster clump at (-5, 3) mas/yr among 20 spread out field stars
+    pmra = np.concatenate([rng.normal(-5.0, 0.3, 40), rng.uniform(-20.0, 20.0, 20)])
+    pmdec = np.concatenate([rng.normal(3.0, 0.3, 40), rng.uniform(-20.0, 20.0, 20)])
+    parallax = np.concatenate([rng.normal(1.25, 0.05, 40), rng.uniform(0.1, 2.0, 20)])
+    # cluster piled up near 800 pc, field stars scattered; +/-40 pc Bailer-Jones bounds per star
+    distance = np.concatenate([rng.normal(800.0, 20.0, 40), rng.uniform(100.0, 3000.0, 20)])
+    distance_lo = distance - 40.0
+    distance_hi = distance + 40.0
+
+    guess = estimate_membership(pmra, pmdec, parallax, distance, distance_lo, distance_hi)
+
+    self.assertIsNotNone(guess)
+    self.assertAlmostEqual(guess['pmra'], -5.0, delta=0.5)
+    self.assertAlmostEqual(guess['pmdec'], 3.0, delta=0.5)
+    self.assertGreaterEqual(guess['pm_radius'], 0.5)
+    self.assertLessEqual(guess['pm_radius'], 5.0)
+    self.assertLess(guess['parallax_min'], 1.25)
+    self.assertGreater(guess['parallax_max'], 1.25)
+    self.assertLess(guess['distance_min'], 800.0)
+    self.assertGreater(guess['distance_max'], 800.0)
+    self.assertGreaterEqual(guess['distance_min'], 0.0)
+
+  def test_estimate_membership_distance_window_optional(self):
+    rng = np.random.default_rng(42)
+    pmra = np.concatenate([rng.normal(-5.0, 0.3, 40), rng.uniform(-20.0, 20.0, 20)])
+    pmdec = np.concatenate([rng.normal(3.0, 0.3, 40), rng.uniform(-20.0, 20.0, 20)])
+    parallax = np.concatenate([rng.normal(1.25, 0.05, 40), rng.uniform(0.1, 2.0, 20)])
+
+    # no distances supplied - the distance window is left unset
+    guess = estimate_membership(pmra, pmdec, parallax)
+
+    self.assertIsNotNone(guess)
+    self.assertIsNone(guess['distance_min'])
+    self.assertIsNone(guess['distance_max'])
+
+  def test_estimate_membership_too_few_stars(self):
+    pmra = np.full(5, -5.0)
+    pmdec = np.full(5, 3.0)
+    parallax = np.full(5, 1.25)
+
+    self.assertIsNone(estimate_membership(pmra, pmdec, parallax))
+
+  def test_estimate_membership_no_clump(self):
+    rng = np.random.default_rng(7)
+    # spread out field stars only, no concentration to call a cluster
+    pmra = rng.uniform(-20.0, 20.0, 40)
+    pmdec = rng.uniform(-20.0, 20.0, 40)
+    parallax = rng.uniform(0.1, 2.0, 40)
+
+    self.assertIsNone(estimate_membership(pmra, pmdec, parallax))
+
+  def test_estimate_membership_tolerates_negative_parallax(self):
+    rng = np.random.default_rng(11)
+    pmra = rng.normal(0.0, 0.2, 15)
+    pmdec = rng.normal(0.0, 0.2, 15)
+    parallax = rng.normal(-0.05, 0.02, 15)  # a distant clump can measure slightly negative
+
+    guess = estimate_membership(pmra, pmdec, parallax)
+
+    self.assertIsNotNone(guess)
+    self.assertLess(guess['parallax_min'], 0.0)
diff --git a/datalab/datalab_session/utils/catalog_utils.py b/datalab/datalab_session/utils/catalog_utils.py
new file mode 100644
index 0000000..bb9246f
--- /dev/null
+++ b/datalab/datalab_session/utils/catalog_utils.py
@@ -0,0 +1,117 @@
+import logging
+
+import numpy as np
+from astropy import units as u
+from astropy.coordinates import SkyCoord
+from astropy.wcs import WCS
+
+from datalab.datalab_session.exceptions import ClientAlertException
+from datalab.datalab_session.utils.file_utils import get_hdu
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+
+def extract_calibrated_catalog(fits_path: str, basename: str = '') -> dict:
+  """
+  Reads the CAT HDU of a fits file and returns full-precision numpy arrays of the
+  source positions and zero-point calibrated photometry: {ra, dec, mag, magerr}
+
+  - Requires calibrated mag/magerr columns, there is no instrumental flux fallback
+    since an un-zero-pointed magnitude makes a color meaningless
+  - Computes ra/dec from the x/y columns and the SCI WCS when the CAT lacks them
+  - Drops rows with non-finite values in any returned column
+  """
+  cat_data = get_hdu(fits_path, 'CAT').data
+
+  if 'mag' not in cat_data.names or 'magerr' not in cat_data.names:
+    raise ClientAlertException(f'{basename} has no zero-point calibrated magnitudes (CAT mag/magerr), '
+                               'which the HR Diagram requires. Try an image in a calibrated filter.')
+
+  mag = np.asarray(cat_data['mag'], dtype=float)
+  magerr = np.asarray(cat_data['magerr'], dtype=float)
+
+  if 'ra' in cat_data.names and 'dec' in cat_data.names:
+    ra = np.asarray(cat_data['ra'], dtype=float)
+    dec = np.asarray(cat_data['dec'], dtype=float)
+  else:
+    ra, dec = _ra_dec_from_wcs(fits_path, cat_data, basename)
+
+  valid = np.isfinite(ra) & np.isfinite(dec) & np.isfinite(mag) & np.isfinite(magerr)
+  return {'ra': ra[valid], 'dec': dec[valid], 'mag': mag[valid], 'magerr': magerr[valid]}
+
+
+def _ra_dec_from_wcs(fits_path: str, cat_data, basename: str = ''):
+  """
+  Fallback for CAT HDUs without ra/dec columns: compute them from the catalog x/y
+  pixel positions and the SCI extension's WCS solution
+  """
+  if 'x' not in cat_data.names or 'y' not in cat_data.names:
+    raise ClientAlertException(f'{basename} catalog has no ra/dec or x/y columns to locate its sources')
+
+  sci_header = get_hdu(fits_path, 'SCI').header
+  wcs = WCS(sci_header)
+  if not wcs.has_celestial:
+    raise ClientAlertException(f'{basename} catalog has no ra/dec columns and the image has no WCS solution to compute them')
+
+  # origin=1: source extractor catalog x/y positions use the FITS 1-based convention
+  ra, dec = wcs.all_pix2world(cat_data['x'], cat_data['y'], 1)
+  return np.asarray(ra, dtype=float), np.asarray(dec, dtype=float)
+
+
+def cross_match_one_to_one(catalog_a: dict, catalog_b: dict, match_radius_arcsec: float):
+  """
+  Nearest-neighbor sky match of catalog_a sources to catalog_b sources within match_radius_arcsec,
+  deduplicated to one-to-one pairs (when multiple a sources share the same nearest b source,
+  only the closest pair is kept, avoiding double counted stars in crowded fields).
+
+  Catalogs are dicts holding equal-length 'ra' and 'dec' arrays in degrees.
+  Returns (a_indices, b_indices) index arrays of the matched pairs.
+  """
+  if len(catalog_a['ra']) == 0 or len(catalog_b['ra']) == 0:
+    return np.array([], dtype=int), np.array([], dtype=int)
+
+  coords_a = SkyCoord(ra=catalog_a['ra'] * u.deg, dec=catalog_a['dec'] * u.deg)
+  coords_b = SkyCoord(ra=catalog_b['ra'] * u.deg, dec=catalog_b['dec'] * u.deg)
+  nearest_b, sep2d, _ = coords_a.match_to_catalog_sky(coords_b)
+  sep_arcsec = np.atleast_1d(sep2d.arcsec)
+  nearest_b = np.atleast_1d(nearest_b)
+
+  # match_to_catalog_sky returns a nearest neighbor unconditionally, so apply the separation cut,
+  # then keep only the closest a for each b to enforce one-to-one matching
+  best_a_for_b = {}
+  for a_index in np.flatnonzero(sep_arcsec <= match_radius_arcsec):
+    b_index = int(nearest_b[a_index])
+    if b_index not in best_a_for_b or sep_arcsec[a_index] < sep_arcsec[best_a_for_b[b_index]]:
+      best_a_for_b[b_index] = int(a_index)
+
+  pairs = sorted((a_index, b_index) for b_index, a_index in best_a_for_b.items())
+  a_indices = np.array([pair[0] for pair in pairs], dtype=int)
+  b_indices = np.array([pair[1] for pair in pairs], dtype=int)
+  return a_indices, b_indices
+
+
+def cone_filter(ra, dec, center_ra: float, center_dec: float, radius_arcmin: float):
+  """
+  Returns a boolean mask of the (ra, dec) points within radius_arcmin of the center point.
+  All coordinates in degrees.
+  """
+  center = SkyCoord(ra=center_ra * u.deg, dec=center_dec * u.deg)
+  points = SkyCoord(ra=np.asarray(ra) * u.deg, dec=np.asarray(dec) * u.deg)
+  return center.separation(points).arcmin <= radius_arcmin
+
+
+def wcs_contains_point(fits_path: str, ra: float, dec: float) -> bool:
+  """
+  Best-effort check of whether (ra, dec) falls inside the image's WCS footprint.
+  Returns True when the WCS is missing or unusable so an inconclusive check never blocks an operation.
+  """
+  try:
+    sci_header = get_hdu(fits_path, 'SCI').header
+    wcs = WCS(sci_header)
+    if not wcs.has_celestial:
+      return True
+    return bool(wcs.footprint_contains(SkyCoord(ra=ra * u.deg, dec=dec * u.deg)))
+  except Exception as e:
+    log.warning(f'Could not check WCS footprint for {fits_path}: {e}')
+    return True
diff --git a/datalab/datalab_session/utils/gaia.py b/datalab/datalab_session/utils/gaia.py
new file mode 100644
index 0000000..f3d42db
--- /dev/null
+++ b/datalab/datalab_session/utils/gaia.py
@@ -0,0 +1,227 @@
+import logging
+
+import numpy as np
+from django.core.cache import cache
+
+from datalab.datalab_session.exceptions import ClientAlertException
+
+log = logging.getLogger()
+log.setLevel(logging.INFO)
+
+GAIA_TABLE = 'gaiadr3.gaia_source'
+GAIA_SYNTH_TABLE = 'gaiadr3.synthetic_photometry_gspc'
+# Bailer-Jones et al. (2021) geometric distances (external.gaiaedr3_distance): a Bayesian
+# posterior with a direction-dependent distance prior, so the estimate is always positive and
+# well behaved even where the parallax is negative or low-significance.
+# r_med_geo is the point estimate (parsecs); r_lo_geo / r_hi_geo are the 16th / 84th
+# percentiles (asymmetric, 1-sigma-like bounds). source_ids match gaiadr3.gaia_source.
+# Photogeometric columns (r_med_photogeo,...) are sharper where BP/RP photometry is good but bake in a Galactic CMD population model;
+# geometric is the robust default and assumes nothing photometric about the sources.
+GAIA_DISTANCE_TABLE = 'external.gaiaedr3_distance'
+GAIA_DISTANCE_COLUMNS = ['r_med_geo', 'r_lo_geo', 'r_hi_geo']
+GAIA_SOURCE_COLUMNS = ['ra', 'dec', 'pmra', 'pmra_error', 'pmdec', 'pmdec_error', 'parallax', 'parallax_error',
+                       'phot_g_mean_mag']
+# Synthetic SDSS-system AB magnitudes from the Gaia Synthetic Photometry Catalogue (computed
+# from the BP/RP spectra, so only sources with published spectra have them, G <~ 17.65).
+# GSPC standardizes griz to the SDSS system - within ~0.03 mag of the Pan-STARRS1 system the
+# images are calibrated to, the same order as BANZAI's color-term-free zero-pointing
+GAIA_SYNTH_BANDS = ['g_sdss', 'r_sdss', 'i_sdss', 'z_sdss']
+# columns queried per source; the result dict also carries a {band}_mag_error for each synth band
+GAIA_COLUMNS = GAIA_SOURCE_COLUMNS + GAIA_DISTANCE_COLUMNS + [f'{band}_mag' for band in GAIA_SYNTH_BANDS]
+# systematic floor on synthetic magnitude errors: SDSS<->PS1 system difference + GSPC calibration
+GAIA_SYNTH_MAG_ERROR_FLOOR = 0.03
+# RUWE < 1.4 is the standard threshold for well behaved astrometric solutions from Lindegren's Gaia astrometry notes
+GAIA_RUWE_LIMIT = 1.4
+# Mean Gaia DR3 parallax zero-point offset (Lindegren et al. 2021): observed parallaxes
+# run ~17 microarcsec too small, so add it back
+GAIA_PARALLAX_ZERO_POINT_MAS = 0.017
+# Gaia DR3 is a static catalog, so cached cone searches stay valid; cache them separately from
+# the operation cache so unrelated input tweaks don't re-hit the TAP service
+GAIA_CACHE_DURATION = 60 * 60 * 24 * 30  # 30 days
+
+# membership_guess tuning
+MIN_GAIA_MATCHES_FOR_GUESS = 10  # too few matched stars to call a clump
+PM_HISTOGRAM_HALF_WIDTH = 25.0   # mas/yr searched around the median proper motion
+PM_HISTOGRAM_BIN = 1.0           # mas/yr cell size, ~cluster clump scale
+MIN_CLUMP_COUNT = 5              # peak cells below this are noise, not a cluster
+PM_RADIUS_BOUNDS = (0.5, 5.0)    # mas/yr allowed range for the suggested clump radius
+
+
+def gaia_cone_search(center_ra: float, center_dec: float, radius_arcmin: float) -> dict:
+  """
+  Gaia DR3 cone search around (center_ra, center_dec) degrees, returning a dict of numpy
+  arrays for GAIA_COLUMNS with the RUWE cut and parallax zero-point correction applied.
+  Missing values (e.g. no proper motion solution) are np.nan.
+
+  Results are cached by field + radius. Failures raise ClientAlertException so the
+  operation fails visibly and can be re-run, rather than caching a degraded result.
+  """
+  # versioned key: whenever GAIA_COLUMNS changes (v2 added phot_g_mean_mag, v3 added the
+  # Bailer-Jones distance columns) a month-old cached response would be missing the new keys
+  cache_key = f'gaia_dr3_cone_v3_{center_ra:.4f}_{center_dec:.4f}_{radius_arcmin:.3f}'
+  gaia_data = cache.get(cache_key)
+  if gaia_data is not None:
+    return gaia_data
+
+  gaia_data = _query_gaia(center_ra, center_dec, radius_arcmin)
+  cache.set(cache_key, gaia_data, GAIA_CACHE_DURATION)
+  return gaia_data
+
+
+def _query_gaia(center_ra: float, center_dec: float, radius_arcmin: float) -> dict:
+  # astroquery is imported lazily so that Django startup (which imports every data operation
+  # to build available_operations) doesn't pay its import cost
+  from astroquery.gaia import Gaia
+
+  source_columns = ', '.join(f'g.{column}' for column in GAIA_SOURCE_COLUMNS)
+  # raw Bailer-Jones column names
+  distance_columns = ', '.join(f'd.{column}' for column in GAIA_DISTANCE_COLUMNS)
+  synth_columns = ', '.join(f's.{band}_mag, s.{band}_flux, s.{band}_flux_error'
+                            for band in GAIA_SYNTH_BANDS)
+  query = f"""
+    SELECT {source_columns}, {distance_columns}, {synth_columns}
+    FROM {GAIA_TABLE} AS g
+    LEFT JOIN {GAIA_DISTANCE_TABLE} AS d ON d.source_id = g.source_id
+    LEFT JOIN {GAIA_SYNTH_TABLE} AS s ON s.source_id = g.source_id
+    WHERE CONTAINS(POINT('ICRS', g.ra, g.dec), CIRCLE('ICRS', {center_ra}, {center_dec}, {radius_arcmin / 60.0})) = 1
+    AND g.ruwe < {GAIA_RUWE_LIMIT}
+  """
+  try:
+    # This launches off an async query to the GAIA service
+    job = Gaia.launch_job_async(query, verbose=False)
+    # This waits synchronously for the results of the GAIA query, which is fine since this operation happens in a worker.
+    # If this takes longer than the timeout limit of the worker task (1 hour), this task will die.
+    table = job.get_results()
+  except Exception as e:
+    log.error(f'Gaia cone search failed for ({center_ra}, {center_dec}) r={radius_arcmin}\': {e}')
+    raise ClientAlertException('Could not fetch Gaia data for the cluster field - the Gaia archive may '
+                               'be temporarily unavailable, try re-running the operation later.')
+
+  def column_values(name):
+    # Get rid of nans from the returned data
+    return np.ma.filled(np.ma.masked_invalid(table[name].data), np.nan).astype(float)
+
+  gaia_data = {column: column_values(column) for column in GAIA_COLUMNS}
+  # Adjust the parallax by the adjustment factor
+  gaia_data['parallax'] = gaia_data['parallax'] + GAIA_PARALLAX_ZERO_POINT_MAS
+  # propagate synthetic flux errors to magnitude errors, on a systematic floor
+  for band in GAIA_SYNTH_BANDS:
+    flux = column_values(f'{band}_flux')
+    flux_error = column_values(f'{band}_flux_error')
+    with np.errstate(divide='ignore', invalid='ignore'):
+      mag_error = 2.5 / np.log(10.0) * flux_error / flux
+    gaia_data[f'{band}_mag_error'] = np.sqrt(mag_error ** 2 + GAIA_SYNTH_MAG_ERROR_FLOOR ** 2)
+
+  log.info(f'Gaia cone search returned {len(gaia_data["ra"])} sources '
+           f'for ({center_ra}, {center_dec}) r={radius_arcmin}\'')
+  return gaia_data
+
+
+def _member_distance_window(distance, distance_lo, distance_hi, finite_pm, members):
+  """
+  Builds a (distance_min, distance_max) parsec window from the clump members' Bailer-Jones
+  distances, or (None, None) when distances weren't supplied or too few members have one.
+
+  Members scatter around the true cluster distance from both real depth and measurement error,
+  so the half-window is 3x the larger of the robust scatter of the r_med_geo point estimates and
+  the members' typical per-star uncertainty (median (r_hi_geo - r_lo_geo) / 2). distance_min is
+  clamped non-negative since Bailer-Jones distances are always positive.
+  """
+  if distance is None:
+    return None, None
+  distance = np.asarray(distance, dtype=float)
+  distance_lo = np.asarray(distance_lo, dtype=float)
+  distance_hi = np.asarray(distance_hi, dtype=float)
+
+  member_distance = distance[finite_pm][members]
+  member_lo = distance_lo[finite_pm][members]
+  member_hi = distance_hi[finite_pm][members]
+  has_distance = np.isfinite(member_distance) & (member_distance > 0)
+  if has_distance.sum() < 3:
+    return None, None
+
+  distances = member_distance[has_distance]
+  distance_median = np.median(distances)
+  robust_scatter = 1.4826 * np.median(np.abs(distances - distance_median))
+  # per-star measurement uncertainty floor, using the asymmetric lo/hi bounds where present
+  half_widths = (member_hi[has_distance] - member_lo[has_distance]) / 2.0
+  half_widths = half_widths[np.isfinite(half_widths) & (half_widths > 0)]
+  per_star_sigma = np.median(half_widths) if len(half_widths) else 0.0
+  distance_sigma = max(robust_scatter, per_star_sigma)
+
+  distance_min = round(float(max(distance_median - 3.0 * distance_sigma, 0.0)), 1)
+  distance_max = round(float(distance_median + 3.0 * distance_sigma), 1)
+  return distance_min, distance_max
+
+
+def estimate_membership(pmra, pmdec, parallax, distance=None, distance_lo=None, distance_hi=None):
+  """
+  Suggests a default cluster membership selection from the proper motions, parallaxes, and
+  Bailer-Jones distances of the Gaia-matched stars: the dominant proper-motion clump (2D
+  histogram peak, robust to the cluster being a minority of the field) plus parallax and
+  distance windows around the clump members.
+
+  distance / distance_lo / distance_hi are the Bailer-Jones r_med_geo point estimate and its
+  r_lo_geo / r_hi_geo 16th / 84th percentile bounds (parsecs); pass them to also get a distance
+  window. When omitted (or too few members have a distance) the distance window is left None.
+
+  Returns {'pmra', 'pmdec', 'pm_radius', 'parallax_min', 'parallax_max', 'distance_min',
+  'distance_max'} in mas(/yr) and parsecs, or None when there are too few stars or no clear
+  clump - the user then selects manually. Negative parallaxes are tolerated throughout.
+  """
+  pmra = np.asarray(pmra, dtype=float)
+  pmdec = np.asarray(pmdec, dtype=float)
+  parallax = np.asarray(parallax, dtype=float)
+
+  finite_pm = np.isfinite(pmra) & np.isfinite(pmdec)
+  if finite_pm.sum() < MIN_GAIA_MATCHES_FOR_GUESS:
+    return None
+  pmra_finite = pmra[finite_pm]
+  pmdec_finite = pmdec[finite_pm]
+
+  # histogram the proper motions around their median and take the densest cell as the clump
+  bins = np.arange(-PM_HISTOGRAM_HALF_WIDTH, PM_HISTOGRAM_HALF_WIDTH + PM_HISTOGRAM_BIN, PM_HISTOGRAM_BIN)
+  median_pmra, median_pmdec = np.median(pmra_finite), np.median(pmdec_finite)
+  histogram, pmra_edges, pmdec_edges = np.histogram2d(pmra_finite - median_pmra, pmdec_finite - median_pmdec, bins=(bins, bins))
+  peak_index = np.unravel_index(np.argmax(histogram), histogram.shape)
+  peak_pmra = median_pmra + (pmra_edges[peak_index[0]] + pmra_edges[peak_index[0] + 1]) / 2.0
+  peak_pmdec = median_pmdec + (pmdec_edges[peak_index[1]] + pmdec_edges[peak_index[1] + 1]) / 2.0
+
+  # judge the clump by the stars within a small neighborhood of the peak cell rather than the
+  # single cell count, so a clump straddling bin edges isn't split below the threshold
+  near_peak = np.hypot(pmra_finite - peak_pmra, pmdec_finite - peak_pmdec) <= 2.0 * PM_HISTOGRAM_BIN
+  if near_peak.sum() < MIN_CLUMP_COUNT:
+    return None
+
+  # refine the clump center with the stars near the peak, then size the radius from their scatter
+  clump_pmra = float(np.median(pmra_finite[near_peak]))
+  clump_pmdec = float(np.median(pmdec_finite[near_peak]))
+  clump_distances = np.hypot(pmra_finite[near_peak] - clump_pmra, pmdec_finite[near_peak] - clump_pmdec)
+  robust_sigma = 1.4826 * np.median(np.abs(clump_distances - np.median(clump_distances)))
+  pm_radius = float(np.clip(3.0 * max(robust_sigma, np.median(clump_distances)), *PM_RADIUS_BOUNDS))
+
+  # parallax window from the clump members with measured parallaxes
+  members = np.hypot(pmra_finite - clump_pmra, pmdec_finite - clump_pmdec) <= pm_radius
+  member_parallaxes = parallax[finite_pm][members]
+  member_parallaxes = member_parallaxes[np.isfinite(member_parallaxes)]
+  if len(member_parallaxes) >= 3:
+    parallax_median = np.median(member_parallaxes)
+    parallax_sigma = max(1.4826 * np.median(np.abs(member_parallaxes - parallax_median)), 0.05)
+    parallax_min = round(float(parallax_median - 3.0 * parallax_sigma), 4)
+    parallax_max = round(float(parallax_median + 3.0 * parallax_sigma), 4)
+  else:
+    parallax_min = None
+    parallax_max = None
+
+  # distance window from the clump members' Bailer-Jones distances, when provided
+  distance_min, distance_max = _member_distance_window(distance, distance_lo, distance_hi, finite_pm, members)
+
+  return {
+    'pmra': round(clump_pmra, 3),
+    'pmdec': round(clump_pmdec, 3),
+    'pm_radius': round(pm_radius, 3),
+    'parallax_min': parallax_min,
+    'parallax_max': parallax_max,
+    'distance_min': distance_min,
+    'distance_max': distance_max,
+  }
diff --git a/datalab/settings.py b/datalab/settings.py
index 9528671..b82242e 100644
--- a/datalab/settings.py
+++ b/datalab/settings.py
@@ -234,4 +234,4 @@ def get_list_from_env(variable, default=None):
 
 CORS_ORIGIN_ALLOW_ALL = True
 CORS_ALLOW_CREDENTIALS = True
-CSRF_TRUSTED_ORIGINS = get_list_from_env('CSRF_TRUSTED_ORIGINS', 'http://localhost:8080,http://127.0.0.1:8000,http://127.0.0.1:8001,http://localhost:8000,')
+CSRF_TRUSTED_ORIGINS = get_list_from_env('CSRF_TRUSTED_ORIGINS', 'http://localhost:8080,http://127.0.0.1:8080,http://127.0.0.1:8081,http://localhost:8081,')
diff --git a/poetry.lock b/poetry.lock
index 1fb9f3c..ba1df13 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,143 +1,144 @@
-# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.3.1 and should not be changed by hand.
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.6.1"
+version = "2.7.1"
 description = "Happy Eyeballs for asyncio"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
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 ]
 
 [package.dependencies]
@@ -147,10 +148,11 @@ attrs = ">=17.3.0"
 frozenlist = ">=1.1.1"
 multidict = ">=4.5,<7.0"
 propcache = ">=0.2.0"
+typing_extensions = {version = ">=4.4", markers = "python_version < \"3.13\""}
 yarl = ">=1.17.0,<2.0"
 
 [package.extras]
-speedups = ["Brotli (>=1.2)", "aiodns (>=3.3.0)", "backports.zstd", "brotlicffi (>=1.2)"]
+speedups = ["Brotli (>=1.2) ; platform_python_implementation == \"CPython\" and sys_platform != \"android\" and sys_platform != \"ios\"", "aiodns (>=3.3.0) ; sys_platform != \"android\" and sys_platform != \"ios\"", "backports.zstd ; platform_python_implementation == \"CPython\" and python_version < \"3.14\" and sys_platform != \"android\" and sys_platform != \"ios\"", "brotlicffi (>=1.2) ; platform_python_implementation != \"CPython\""]
 
 [[package]]
 name = "aiosignal"
@@ -158,6 +160,7 @@ version = "1.4.0"
 description = "aiosignal: a list of registered asynchronous callbacks"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e"},
     {file = "aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7"},
@@ -169,13 +172,14 @@ typing-extensions = {version = ">=4.2", markers = "python_version < \"3.13\""}
 
 [[package]]
 name = "asgiref"
-version = "3.11.0"
+version = "3.11.1"
 description = "ASGI specs, helper code, and adapters"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
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-    {file = "asgiref-3.11.0.tar.gz", hash = "sha256:13acff32519542a1736223fb79a715acdebe24286d98e8b164a73085f40da2c4"},
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+    {file = "asgiref-3.11.1.tar.gz", hash = "sha256:5f184dc43b7e763efe848065441eac62229c9f7b0475f41f80e207a114eda4ce"},
 ]
 
 [package.extras]
@@ -187,6 +191,7 @@ version = "6.1.7"
 description = "Astronomy and astrophysics core library"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "astropy-6.1.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:be954c5f7707a089609053665aeb76493b79e5c4753c39486761bc6d137bf040"},
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@@ -228,7 +233,7 @@ PyYAML = ">=3.13"
 
 [package.extras]
 all = ["asdf-astropy (>=0.3)", "astropy[recommended]", "astropy[typing]", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2023.4.0)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "mpmath", "pandas", "pre-commit", "pyarrow (>=7.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2023.4.0)", "sortedcontainers"]
-docs = ["Jinja2 (>=3.1.3)", "astropy[recommended]", "matplotlib (>=3.9.1)", "numpy (<2.0)", "pytest (>=7.0)", "sphinx", "sphinx-astropy[confv2] (>=1.9.1)", "sphinx-changelog (>=1.2.0)", "sphinx_design", "sphinxcontrib-globalsubs (>=0.1.1)", "tomli"]
+docs = ["Jinja2 (>=3.1.3)", "astropy[recommended]", "matplotlib (>=3.9.1)", "numpy (<2.0)", "pytest (>=7.0)", "sphinx", "sphinx-astropy[confv2] (>=1.9.1)", "sphinx-changelog (>=1.2.0)", "sphinx_design", "sphinxcontrib-globalsubs (>=0.1.1)", "tomli ; python_version < \"3.11\""]
 recommended = ["matplotlib (>=3.5.0,!=3.5.2)", "scipy (>=1.8)"]
 test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "threadpoolctl"]
 test-all = ["array-api-strict", "astropy[test]", "coverage[toml]", "ipython (>=4.2)", "objgraph", "sgp4 (>=2.3)", "skyfield (>=1.20)"]
@@ -240,6 +245,7 @@ version = "1.1.3"
 description = "BSD-licensed HEALPix for Astropy"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "astropy_healpix-1.1.3-cp310-abi3-macosx_10_9_x86_64.whl", hash = "sha256:182b0b6a833afa9daa0c73c8394a2a067da7f42cd27c838e1ced7592979ae934"},
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@@ -268,25 +274,54 @@ test = ["hypothesis", "pytest-astropy"]
 
 [[package]]
 name = "astropy-iers-data"
-version = "0.2026.1.12.0.42.13"
+version = "0.2026.7.13.0.54.2"
 description = "IERS Earth Rotation and Leap Second tables for the astropy core package"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
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+docs = ["fsspec[http]", "matplotlib", "scipy", "sphinx-astropy (>=1.5)", "sphinx-automodapi (<0.18)"]
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+
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+groups = ["main"]
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 [package.dependencies]
@@ -450,135 +546,117 @@ pycparser = {version = "*", markers = "implementation_name != \"PyPy\""}
 
 [[package]]
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 optional = false
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 [[package]]
 name = "click"
-version = "8.3.1"
+version = "8.4.2"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -590,6 +668,7 @@ version = "3.1.2"
 description = "Pickler class to extend the standard pickle.Pickler functionality"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a"},
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@@ -601,75 +680,141 @@ version = "0.4.6"
 description = "Cross-platform colored terminal text."
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
+groups = ["main", "test"]
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
+markers = {main = "platform_system == \"Windows\"", test = "sys_platform == \"win32\""}
+
+[[package]]
+name = "cryptography"
+version = "49.0.0"
+description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
+optional = false
+python-versions = "!=3.9.0,!=3.9.1,>=3.9"
+groups = ["main"]
+markers = "sys_platform == \"linux\""
+files = [
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+]
+
+[package.dependencies]
+cffi = {version = ">=2.0.0", markers = "platform_python_implementation != \"PyPy\""}
+
+[package.extras]
+ssh = ["bcrypt (>=3.1.5)"]
 
 [[package]]
 name = "dask"
-version = "2026.1.2"
+version = "2026.7.1"
 description = "Parallel PyData with Task Scheduling"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
 click = ">=8.1"
 cloudpickle = ">=3.0.0"
-fsspec = ">=2021.09.0"
+fsspec = ">=2021.9.0"
 importlib_metadata = {version = ">=4.13.0", markers = "python_version < \"3.12\""}
 numpy = {version = ">=1.24", optional = true, markers = "extra == \"array\""}
 packaging = ">=20.0"
-pandas = {version = ">=2.0", optional = true, markers = "extra == \"dataframe\""}
 partd = ">=1.4.0"
-pyarrow = {version = ">=16.0", optional = true, markers = "extra == \"dataframe\""}
-pyyaml = ">=5.3.1"
+pyyaml = ">=5.4.1"
 toolz = ">=0.12.0"
 
 [package.extras]
 array = ["numpy (>=1.24)"]
-complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)", "pyarrow (>=16.0)"]
+complete = ["dask[array,dataframe,diagnostics,distributed]", "lz4 (>=4.3.2)"]
 dataframe = ["dask[array]", "pandas (>=2.0)", "pyarrow (>=16.0)"]
 diagnostics = ["bokeh (>=3.1.0)", "jinja2 (>=2.10.3)"]
-distributed = ["distributed (>=2026.1.2,<2026.1.3)"]
+distributed = ["distributed (>=2026.7.1,<2026.7.2)"]
 test = ["pandas[test]", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytest-rerunfailures", "pytest-timeout", "pytest-xdist"]
 
 [[package]]
 name = "dask-image"
-version = "2025.11.0"
+version = "2026.5.0"
 description = "Distributed image processing"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
-dask = {version = ">=2024.4.1", extras = ["array", "dataframe"]}
+dask = {version = ">=2024.4.1", extras = ["array"]}
 numpy = ">=1.18"
-pandas = ">=2.0.0"
 pims = ">=0.4.1"
 scipy = ">=1.7.0"
-tifffile = ">=2018.10.18"
+tifffile = ">=2020.10.1"
 
 [package.extras]
+dataframe = ["dask[dataframe] (>=2024.4.1)", "pandas (>=2.0.0)"]
 gpu = ["cupy (>=9.0.0)"]
 test = ["Flake8-pyproject", "build (>=1.2.1)", "coverage (>=7.2.1)", "flake8 (>=6.0.0)", "pytest (>=7.2.2)", "pytest-cov (>=4.0.0)", "pytest-flake8 (>=1.1.1)", "pytest-timeout (>=2.3.1)", "twine (>=3.1.1)"]
 
 [[package]]
 name = "django"
-version = "4.2.27"
+version = "4.2.30"
 description = "A high-level Python web framework that encourages rapid development and clean, pragmatic design."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
-    {file = "django-4.2.27-py3-none-any.whl", hash = "sha256:f393a394053713e7d213984555c5b7d3caeee78b2ccb729888a0774dff6c11a8"},
-    {file = "django-4.2.27.tar.gz", hash = "sha256:b865fbe0f4a3d1ee36594c5efa42b20db3c8bbb10dff0736face1c6e4bda5b92"},
+    {file = "django-4.2.30-py3-none-any.whl", hash = "sha256:4d07aaf1c62f9984842b67c2874ebbf7056a17be253860299b93ae1881faad65"},
+    {file = "django-4.2.30.tar.gz", hash = "sha256:4ebc7a434e3819db6cf4b399fb5b3f536310a30e8486f08b66886840be84b37c"},
 ]
 
 [package.dependencies]
@@ -687,6 +832,7 @@ version = "4.9.0"
 description = "django-cors-headers is a Django application for handling the server headers required for Cross-Origin Resource Sharing (CORS)."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "django_cors_headers-4.9.0-py3-none-any.whl", hash = "sha256:15c7f20727f90044dcee2216a9fd7303741a864865f0c3657e28b7056f61b449"},
     {file = "django_cors_headers-4.9.0.tar.gz", hash = "sha256:fe5d7cb59fdc2c8c646ce84b727ac2bca8912a247e6e68e1fb507372178e59e8"},
@@ -702,6 +848,7 @@ version = "0.11.6"
 description = "A Django app for Dramatiq."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "django_dramatiq-0.11.6-py3-none-any.whl", hash = "sha256:9843f5e4d019bf8f8c89e78b39ba037b3552f17f587f49633cc5e13774ac0ab0"},
     {file = "django_dramatiq-0.11.6.tar.gz", hash = "sha256:f8b47a1654c4a8a1a0b4d3215b6b3ec189cb28dd4dec5afb5437ada48ee38ff1"},
@@ -720,6 +867,7 @@ version = "3.2.3"
 description = "Extensions for Django"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "django-extensions-3.2.3.tar.gz", hash = "sha256:44d27919d04e23b3f40231c4ab7af4e61ce832ef46d610cc650d53e68328410a"},
     {file = "django_extensions-3.2.3-py3-none-any.whl", hash = "sha256:9600b7562f79a92cbf1fde6403c04fee314608fefbb595502e34383ae8203401"},
@@ -734,6 +882,7 @@ version = "23.5"
 description = "Django-filter is a reusable Django application for allowing users to filter querysets dynamically."
 optional = false
 python-versions = ">=3.7"
+groups = ["main"]
 files = [
     {file = "django-filter-23.5.tar.gz", hash = "sha256:67583aa43b91fe8c49f74a832d95f4d8442be628fd4c6d65e9f811f5153a4e5c"},
     {file = "django_filter-23.5-py3-none-any.whl", hash = "sha256:99122a201d83860aef4fe77758b69dda913e874cc5e0eaa50a86b0b18d708400"},
@@ -748,6 +897,7 @@ version = "5.4.0"
 description = "Full featured redis cache backend for Django."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "django-redis-5.4.0.tar.gz", hash = "sha256:6a02abaa34b0fea8bf9b707d2c363ab6adc7409950b2db93602e6cb292818c42"},
     {file = "django_redis-5.4.0-py3-none-any.whl", hash = "sha256:ebc88df7da810732e2af9987f7f426c96204bf89319df4c6da6ca9a2942edd5b"},
@@ -762,13 +912,14 @@ hiredis = ["redis[hiredis] (>=3,!=4.0.0,!=4.0.1)"]
 
 [[package]]
 name = "djangorestframework"
-version = "3.16.1"
+version = "3.17.1"
 description = "Web APIs for Django, made easy."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "djangorestframework-3.16.1-py3-none-any.whl", hash = "sha256:33a59f47fb9c85ede792cbf88bde71893bcda0667bc573f784649521f1102cec"},
-    {file = "djangorestframework-3.16.1.tar.gz", hash = "sha256:166809528b1aced0a17dc66c24492af18049f2c9420dbd0be29422029cfc3ff7"},
+    {file = "djangorestframework-3.17.1-py3-none-any.whl", hash = "sha256:c3c74dd3e83a5a3efc37b3c18d92bd6f86a6791c7b7d4dff62bb068500e76457"},
+    {file = "djangorestframework-3.17.1.tar.gz", hash = "sha256:a6def5f447fe78ff853bff1d47a3c59bf38f5434b031780b351b0c73a62db1a5"},
 ]
 
 [package.dependencies]
@@ -780,6 +931,7 @@ version = "0.8.1.post1"
 description = "Python package for configuring a python package"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "donfig-0.8.1.post1-py3-none-any.whl", hash = "sha256:2a3175ce74a06109ff9307d90a230f81215cbac9a751f4d1c6194644b8204f9d"},
     {file = "donfig-0.8.1.post1.tar.gz", hash = "sha256:3bef3413a4c1c601b585e8d297256d0c1470ea012afa6e8461dc28bfb7c23f52"},
@@ -798,6 +950,7 @@ version = "1.18.0"
 description = "Background Processing for Python 3."
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "dramatiq-1.18.0-py3-none-any.whl", hash = "sha256:d360f608aa3cd06f5db714bfcd23825dc7098bacfee52aca536b0bb0faae3c69"},
     {file = "dramatiq-1.18.0.tar.gz", hash = "sha256:5ea436b6e50dae64d4de04f1eb519ad239a6b1ba6315ba1dce1c0c4c1ebedfaf"},
@@ -821,6 +974,7 @@ version = "0.93.5"
 description = "Nested resources for the Django Rest Framework"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "drf-nested-routers-0.93.5.tar.gz", hash = "sha256:1407565abc7bada37c162c7e11bf214ae71625a17fdec6d9a47a17f4a3627d32"},
     {file = "drf_nested_routers-0.93.5-py2.py3-none-any.whl", hash = "sha256:9a6813554020134a02e62f8c2934b2047717f7da06f8b801752c521e43735c63"},
@@ -836,6 +990,7 @@ version = "12.0.1"
 description = "Faker is a Python package that generates fake data for you."
 optional = false
 python-versions = ">=3.6"
+groups = ["test"]
 files = [
     {file = "Faker-12.0.1-py3-none-any.whl", hash = "sha256:1dc2811f20e163892fefe7006f2ce00778f8099a40aee265bfa60a13400de63d"},
     {file = "Faker-12.0.1.tar.gz", hash = "sha256:aa7103805ae793277abbb85da9f6f05e76a1a295a9384a8e17c2fba2b3a690cb"},
@@ -850,6 +1005,7 @@ version = "0.4.4"
 description = "Align a sequence of astronomical FITS files based on sources extracted in each image. The aligned files will be geometrically reprojected so all the images are the same size and shape."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "fits_align-0.4.4-py3-none-any.whl", hash = "sha256:5a195f9ec9072df2778181e44a22ee433200ce6baa37d35a806e73441012a1fb"},
     {file = "fits_align-0.4.4.tar.gz", hash = "sha256:22df2973d25a60325fb9c41c89247a27eb2396e31e8dd72aa96e3d2423cb6814"},
@@ -866,6 +1022,7 @@ version = "0.4.11"
 description = "Common libraries for the conversion and scaling of fits images"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "fits2image-0.4.11-py3-none-any.whl", hash = "sha256:ce6047934a570915a7d0a02fc5e83d93f961fc53c620a86aeb69bac1da991a9f"},
     {file = "fits2image-0.4.11.tar.gz", hash = "sha256:9ac63a3e01d17a36091e3dff394dd3dfc03acfba4fb69e93b340d958425e5450"},
@@ -882,6 +1039,7 @@ version = "1.8.0"
 description = "A list-like structure which implements collections.abc.MutableSequence"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:b37f6d31b3dcea7deb5e9696e529a6aa4a898adc33db82da12e4c60a7c4d2011"},
     {file = "frozenlist-1.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ef2b7b394f208233e471abc541cc6991f907ffd47dc72584acee3147899d6565"},
@@ -1021,6 +1179,7 @@ version = "2025.12.0"
 description = "File-system specification"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "fsspec-2025.12.0-py3-none-any.whl", hash = "sha256:8bf1fe301b7d8acfa6e8571e3b1c3d158f909666642431cc78a1b7b4dbc5ec5b"},
     {file = "fsspec-2025.12.0.tar.gz", hash = "sha256:c505de011584597b1060ff778bb664c1bc022e87921b0e4f10cc9c44f9635973"},
@@ -1051,7 +1210,7 @@ smb = ["smbprotocol"]
 ssh = ["paramiko"]
 test = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "numpy", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "requests"]
 test-downstream = ["aiobotocore (>=2.5.4,<3.0.0)", "dask[dataframe,test]", "moto[server] (>4,<5)", "pytest-timeout", "xarray"]
-test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard"]
+test-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "cloudpickle", "dask", "distributed", "dropbox", "dropboxdrivefs", "fastparquet", "fusepy", "gcsfs", "jinja2", "kerchunk", "libarchive-c", "lz4", "notebook", "numpy", "ocifs", "pandas", "panel", "paramiko", "pyarrow", "pyarrow (>=1)", "pyftpdlib", "pygit2", "pytest", "pytest-asyncio (!=0.22.0)", "pytest-benchmark", "pytest-cov", "pytest-mock", "pytest-recording", "pytest-rerunfailures", "python-snappy", "requests", "smbprotocol", "tqdm", "urllib3", "zarr", "zstandard ; python_version < \"3.14\""]
 tqdm = ["tqdm"]
 
 [[package]]
@@ -1060,6 +1219,7 @@ version = "1.8.0"
 description = "A python wrapper of the C library 'Google CRC32C'"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "google_crc32c-1.8.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:0470b8c3d73b5f4e3300165498e4cf25221c7eb37f1159e221d1825b6df8a7ff"},
     {file = "google_crc32c-1.8.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:119fcd90c57c89f30040b47c211acee231b25a45d225e3225294386f5d258288"},
@@ -1102,6 +1262,7 @@ version = "2.4.0"
 description = "Python wrapper for hiredis"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "hiredis-2.4.0-cp310-cp310-macosx_10_15_universal2.whl", hash = "sha256:aee6c4e8f670ea685345ce4ca01c574a52e0a4318af2b8cdd563de9567731056"},
     {file = "hiredis-2.4.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:76503a0edaf3d1557518127511e69e5d9fa37b6ff15598b0d9d9c2db18b08a41"},
@@ -1199,29 +1360,53 @@ files = [
     {file = "hiredis-2.4.0.tar.gz", hash = "sha256:90d7af678056c7889d86821344d79fec3932a6a1480ebba3d644cb29a3135348"},
 ]
 
+[[package]]
+name = "html5lib"
+version = "1.1"
+description = "HTML parser based on the WHATWG HTML specification"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+groups = ["main"]
+files = [
+    {file = "html5lib-1.1-py2.py3-none-any.whl", hash = "sha256:0d78f8fde1c230e99fe37986a60526d7049ed4bf8a9fadbad5f00e22e58e041d"},
+    {file = "html5lib-1.1.tar.gz", hash = "sha256:b2e5b40261e20f354d198eae92afc10d750afb487ed5e50f9c4eaf07c184146f"},
+]
+
+[package.dependencies]
+six = ">=1.9"
+webencodings = "*"
+
+[package.extras]
+all = ["chardet (>=2.2)", "genshi", "lxml ; platform_python_implementation == \"CPython\""]
+chardet = ["chardet (>=2.2)"]
+genshi = ["genshi"]
+lxml = ["lxml ; platform_python_implementation == \"CPython\""]
+
 [[package]]
 name = "idna"
-version = "3.11"
+version = "3.18"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
+groups = ["main"]
 files = [
-    {file = "idna-3.11-py3-none-any.whl", hash = "sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea"},
-    {file = "idna-3.11.tar.gz", hash = "sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902"},
+    {file = "idna-3.18-py3-none-any.whl", hash = "sha256:7f952cbe720b688055e3f87de14f5c3e5fdaa8bc3928985c4077ca689de849a2"},
+    {file = "idna-3.18.tar.gz", hash = "sha256:ffb385a7e039654cef1ab9ef32c6fafe283c0c0467bba1d9029738ce4a14a848"},
 ]
 
 [package.extras]
-all = ["flake8 (>=7.1.1)", "mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
+all = ["mypy (>=1.11.2)", "pytest (>=8.3.2)", "ruff (>=0.6.2)"]
 
 [[package]]
 name = "imageio"
-version = "2.37.2"
+version = "2.37.3"
 description = "Read and write images and video across all major formats. Supports scientific and volumetric data."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "imageio-2.37.2-py3-none-any.whl", hash = "sha256:ad9adfb20335d718c03de457358ed69f141021a333c40a53e57273d8a5bd0b9b"},
-    {file = "imageio-2.37.2.tar.gz", hash = "sha256:0212ef2727ac9caa5ca4b2c75ae89454312f440a756fcfc8ef1993e718f50f8a"},
+    {file = "imageio-2.37.3-py3-none-any.whl", hash = "sha256:46f5bb8522cd421c0f5ae104d8268f569d856b29eb1a13b92829d1970f32c9f0"},
+    {file = "imageio-2.37.3.tar.gz", hash = "sha256:bbb37efbfc4c400fcd534b367b91fcd66d5da639aaa138034431a1c5e0a41451"},
 ]
 
 [package.dependencies]
@@ -1248,26 +1433,28 @@ tifffile = ["tifffile"]
 
 [[package]]
 name = "importlib-metadata"
-version = "8.7.1"
+version = "9.0.0"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.11\""
 files = [
-    {file = "importlib_metadata-8.7.1-py3-none-any.whl", hash = "sha256:5a1f80bf1daa489495071efbb095d75a634cf28a8bc299581244063b53176151"},
-    {file = "importlib_metadata-8.7.1.tar.gz", hash = "sha256:49fef1ae6440c182052f407c8d34a68f72efc36db9ca90dc0113398f2fdde8bb"},
+    {file = "importlib_metadata-9.0.0-py3-none-any.whl", hash = "sha256:2d21d1cc5a017bd0559e36150c21c830ab1dc304dedd1b7ea85d20f45ef3edd7"},
+    {file = "importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc"},
 ]
 
 [package.dependencies]
 zipp = ">=3.20"
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 enabler = ["pytest-enabler (>=3.4)"]
 perf = ["ipython"]
-test = ["flufl.flake8", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
-type = ["mypy (<1.19)", "pytest-mypy (>=1.0.1)"]
+test = ["packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
+type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""]
 
 [[package]]
 name = "iniconfig"
@@ -1275,40 +1462,155 @@ version = "2.3.0"
 description = "brain-dead simple config-ini parsing"
 optional = false
 python-versions = ">=3.10"
+groups = ["test"]
 files = [
     {file = "iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12"},
     {file = "iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730"},
 ]
 
+[[package]]
+name = "jaraco-classes"
+version = "3.4.0"
+description = "Utility functions for Python class constructs"
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
+    {file = "jaraco.classes-3.4.0-py3-none-any.whl", hash = "sha256:f662826b6bed8cace05e7ff873ce0f9283b5c924470fe664fff1c2f00f581790"},
+    {file = "jaraco.classes-3.4.0.tar.gz", hash = "sha256:47a024b51d0239c0dd8c8540c6c7f484be3b8fcf0b2d85c13825780d3b3f3acd"},
+]
+
+[package.dependencies]
+more-itertools = "*"
+
+[package.extras]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-ruff (>=0.2.1)"]
+
+[[package]]
+name = "jaraco-context"
+version = "6.1.2"
+description = "Useful decorators and context managers"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "jaraco_context-6.1.2-py3-none-any.whl", hash = "sha256:bf8150b79a2d5d91ae48629d8b427a8f7ba0e1097dd6202a9059f29a36379535"},
+    {file = "jaraco_context-6.1.2.tar.gz", hash = "sha256:f1a6c9d391e661cc5b8d39861ff077a7dc24dc23833ccee564b234b81c82dfe3"},
+]
+
+[package.dependencies]
+"backports.tarfile" = {version = "*", markers = "python_version < \"3.12\""}
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=3.4)"]
+test = ["jaraco.test (>=5.6.0)", "portend", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""]
+
+[[package]]
+name = "jaraco-functools"
+version = "4.6.0"
+description = "Functools like those found in stdlib"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "jaraco_functools-4.6.0-py3-none-any.whl", hash = "sha256:99e3dc0060c5cbe8fcd1cdb36258e2a65ca40f1566b2033b12abb1bb44dd3c30"},
+    {file = "jaraco_functools-4.6.0.tar.gz", hash = "sha256:880c577ec9720b3a052d5bc611fb9f2269b3d87902ef42440df443b88e443280"},
+]
+
+[package.dependencies]
+more_itertools = "*"
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=3.4)"]
+test = ["jaraco.classes", "pytest (>=6,!=8.1.*)"]
+type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""]
+
+[[package]]
+name = "jeepney"
+version = "0.9.0"
+description = "Low-level, pure Python DBus protocol wrapper."
+optional = false
+python-versions = ">=3.7"
+groups = ["main"]
+markers = "sys_platform == \"linux\""
+files = [
+    {file = "jeepney-0.9.0-py3-none-any.whl", hash = "sha256:97e5714520c16fc0a45695e5365a2e11b81ea79bba796e26f9f1d178cb182683"},
+    {file = "jeepney-0.9.0.tar.gz", hash = "sha256:cf0e9e845622b81e4a28df94c40345400256ec608d0e55bb8a3feaa9163f5732"},
+]
+
+[package.extras]
+test = ["async-timeout ; python_version < \"3.11\"", "pytest", "pytest-asyncio (>=0.17)", "pytest-trio", "testpath", "trio"]
+trio = ["trio"]
+
 [[package]]
 name = "jmespath"
-version = "1.0.1"
+version = "1.1.0"
 description = "JSON Matching Expressions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
+groups = ["main"]
 files = [
-    {file = "jmespath-1.0.1-py3-none-any.whl", hash = "sha256:02e2e4cc71b5bcab88332eebf907519190dd9e6e82107fa7f83b1003a6252980"},
-    {file = "jmespath-1.0.1.tar.gz", hash = "sha256:90261b206d6defd58fdd5e85f478bf633a2901798906be2ad389150c5c60edbe"},
+    {file = "jmespath-1.1.0-py3-none-any.whl", hash = "sha256:a5663118de4908c91729bea0acadca56526eb2698e83de10cd116ae0f4e97c64"},
+    {file = "jmespath-1.1.0.tar.gz", hash = "sha256:472c87d80f36026ae83c6ddd0f1d05d4e510134ed462851fd5f754c8c3cbb88d"},
 ]
 
+[[package]]
+name = "keyring"
+version = "25.7.0"
+description = "Store and access your passwords safely."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "keyring-25.7.0-py3-none-any.whl", hash = "sha256:be4a0b195f149690c166e850609a477c532ddbfbaed96a404d4e43f8d5e2689f"},
+    {file = "keyring-25.7.0.tar.gz", hash = "sha256:fe01bd85eb3f8fb3dd0405defdeac9a5b4f6f0439edbb3149577f244a2e8245b"},
+]
+
+[package.dependencies]
+importlib_metadata = {version = ">=4.11.4", markers = "python_version < \"3.12\""}
+"jaraco.classes" = "*"
+"jaraco.context" = "*"
+"jaraco.functools" = "*"
+jeepney = {version = ">=0.4.2", markers = "sys_platform == \"linux\""}
+pywin32-ctypes = {version = ">=0.2.0", markers = "sys_platform == \"win32\""}
+SecretStorage = {version = ">=3.2", markers = "sys_platform == \"linux\""}
+
+[package.extras]
+check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
+completion = ["shtab (>=1.1.0)"]
+cover = ["pytest-cov"]
+doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+enabler = ["pytest-enabler (>=3.4)"]
+test = ["pyfakefs", "pytest (>=6,!=8.1.*)"]
+type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]
+
 [[package]]
 name = "lazy-loader"
-version = "0.4"
+version = "0.5"
 description = "Makes it easy to load subpackages and functions on demand."
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.9"
+groups = ["main"]
 files = [
-    {file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
-    {file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
+    {file = "lazy_loader-0.5-py3-none-any.whl", hash = "sha256:ab0ea149e9c554d4ffeeb21105ac60bed7f3b4fd69b1d2360a4add51b170b005"},
+    {file = "lazy_loader-0.5.tar.gz", hash = "sha256:717f9179a0dbed357012ddad50a5ad3d5e4d9a0b8712680d4e687f5e6e6ed9b3"},
 ]
 
 [package.dependencies]
 packaging = "*"
 
 [package.extras]
-dev = ["changelist (==0.5)"]
-lint = ["pre-commit (==3.7.0)"]
-test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
+dev = ["changelist (==0.5)", "spin (==0.15)"]
+lint = ["pre-commit (==4.3.0)"]
+test = ["coverage[toml] (>=7.2)", "pytest (>=8.0)", "pytest-cov (>=5.0)"]
 
 [[package]]
 name = "locket"
@@ -1316,6 +1618,7 @@ version = "1.0.0"
 description = "File-based locks for Python on Linux and Windows"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+groups = ["main"]
 files = [
     {file = "locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3"},
     {file = "locket-1.0.0.tar.gz", hash = "sha256:5c0d4c052a8bbbf750e056a8e65ccd309086f4f0f18a2eac306a8dfa4112a632"},
@@ -1327,6 +1630,7 @@ version = "7.2.2"
 description = "Mixer -- Is a fixtures replacement. Supported Django ORM, SqlAlchemy ORM, Mongoengine ODM and custom python objects."
 optional = false
 python-versions = ">=3.7"
+groups = ["test"]
 files = [
     {file = "mixer-7.2.2-py3-none-any.whl", hash = "sha256:8089b8e2d00288c77e622936198f5dd03c8ac1603a1530a4f870dc213363b2ae"},
     {file = "mixer-7.2.2.tar.gz", hash = "sha256:9b3f1a261b56d8f2394f39955f83adbc7ff3ab4bb1065ebfec19a10d3e8501e0"},
@@ -1338,179 +1642,221 @@ Faker = ">=5.4.0,<12.1"
 [package.extras]
 tests = ["Django (>=3.0)", "Flask (>=1.0)", "Marshmallow (>=3.9)", "SQLAlchemy (>=1.1.4)", "flask-sqlalchemy (>=2.1)", "mongoengine (>=0.10.1)", "peewee (>=3.7.0)", "pony (>=0.7)", "psycopg2-binary (>=2.8.4)", "pytest"]
 
+[[package]]
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+
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 [[package]]
 name = "numcodecs"
@@ -1518,6 +1864,7 @@ version = "0.16.5"
 description = "A Python package providing buffer compression and transformation codecs for use in data storage and communication applications."
 optional = false
 python-versions = ">=3.11"
+groups = ["main"]
 files = [
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@@ -1562,6 +1909,7 @@ version = "1.26.4"
 description = "Fundamental package for array computing in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
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@@ -1603,17 +1951,18 @@ files = [
 
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+version = "0.2.4"
 description = "Authentication backends and utilities for the OCS applications"
 optional = false
-python-versions = ">=3.8,<4.0"
+python-versions = "<4.0,>=3.8"
+groups = ["main"]
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 [package.dependencies]
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+django = ">=4.0"
 djangorestframework = ">=3.13,<4.0"
 requests = ">=2,<3"
 
@@ -1623,6 +1972,7 @@ version = "4.11.0.86"
 description = "Wrapper package for OpenCV python bindings."
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
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-    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
+    {version = ">=1.23.5", markers = "python_version == \"3.11\""},
 ]
 
 [[package]]
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-version = "25.0"
+version = "26.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
+groups = ["main", "test"]
 files = [
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 fpx = ["olefile"]
 mic = ["olefile"]
 test-arrow = ["arro3-compute", "arro3-core", "nanoarrow", "pyarrow"]
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 xmp = ["defusedxml"]
 
 [[package]]
@@ -1981,6 +2148,7 @@ version = "0.7"
 description = "Python Image Sequence"
 optional = false
 python-versions = ">=3.7"
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 description = "plugin and hook calling mechanisms for python"
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 python-versions = ">=3.9"
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 description = "Python client for the Prometheus monitoring system."
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 python-versions = ">=3.9"
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 ]
 
 [[package]]
 name = "pyavm"
-version = "0.9.8"
+version = "0.9.9"
 description = "Simple pure-python AVM meta-data handling"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "pyavm-0.9.8-py3-none-any.whl", hash = "sha256:fba0f6cd43e729cb50509ffcf54d7abd2aeb3ce3dd3b1ea40b3e0f6e6eb3d917"},
-    {file = "pyavm-0.9.8.tar.gz", hash = "sha256:ce11c279ec75bdf823d0c386124a1528a5e7b36fa5dd4d26499227939f117f88"},
+    {file = "pyavm-0.9.9-py3-none-any.whl", hash = "sha256:8bba0ee9645a8a9f215af9ceea67b494a6ee1fe380cebdc00ba99d781539ad76"},
+    {file = "pyavm-0.9.9.tar.gz", hash = "sha256:bc0f605d957c1fd6d7765523fcba8b9a72377ac6c51461c2a838fe44600bcd9a"},
 ]
 
 [package.extras]
@@ -2306,13 +2419,15 @@ testall = ["astropy", "numpy", "pillow"]
 
 [[package]]
 name = "pycparser"
-version = "2.23"
+version = "3.0"
 description = "C parser in Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "implementation_name != \"PyPy\""
 files = [
-    {file = "pycparser-2.23-py3-none-any.whl", hash = "sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934"},
-    {file = "pycparser-2.23.tar.gz", hash = "sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2"},
+    {file = "pycparser-3.0-py3-none-any.whl", hash = "sha256:b727414169a36b7d524c1c3e31839a521725078d7b2ff038656844266160a992"},
+    {file = "pycparser-3.0.tar.gz", hash = "sha256:600f49d217304a5902ac3c37e1281c9fe94e4d0489de643a9504c5cdfdfc6b29"},
 ]
 
 [[package]]
@@ -2321,6 +2436,7 @@ version = "2.0.1.5"
 description = "Python bindings for ERFA"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
     {file = "pyerfa-2.0.1.5-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:b282d7c60c4c47cf629c484c17ac504fcb04abd7b3f4dfcf53ee042afc3a5944"},
     {file = "pyerfa-2.0.1.5-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:be1aeb70390dd03a34faf96749d5cabc58437410b4aab7213c512323932427df"},
@@ -2344,20 +2460,18 @@ test = ["pytest", "pytest-doctestplus (>=0.7)"]
 
 [[package]]
 name = "pyjwt"
-version = "2.10.1"
+version = "2.13.0"
 description = "JSON Web Token implementation in Python"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
-    {file = "PyJWT-2.10.1-py3-none-any.whl", hash = "sha256:dcdd193e30abefd5debf142f9adfcdd2b58004e644f25406ffaebd50bd98dacb"},
-    {file = "pyjwt-2.10.1.tar.gz", hash = "sha256:3cc5772eb20009233caf06e9d8a0577824723b44e6648ee0a2aedb6cf9381953"},
+    {file = "pyjwt-2.13.0-py3-none-any.whl", hash = "sha256:66adcc2aff09b3f1bbd95fc1e1577df8ac8723c978552fd43304c8a290ac5728"},
+    {file = "pyjwt-2.13.0.tar.gz", hash = "sha256:41571c89ca91598c79e8ef18a2d07367d4810fbbd6f637794879baf1b7703423"},
 ]
 
 [package.extras]
 crypto = ["cryptography (>=3.4.0)"]
-dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx", "sphinx-rtd-theme", "zope.interface"]
-docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"]
-tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"]
 
 [[package]]
 name = "pytest"
@@ -2365,6 +2479,7 @@ version = "7.4.4"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
+groups = ["test"]
 files = [
     {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
     {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
@@ -2385,6 +2500,7 @@ version = "2.9.0.post0"
 description = "Extensions to the standard Python datetime module"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main", "test"]
 files = [
     {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
     {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
@@ -2394,14 +2510,37 @@ files = [
 six = ">=1.5"
 
 [[package]]
-name = "pytz"
-version = "2025.2"
-description = "World timezone definitions, modern and historical"
+name = "pyvo"
+version = "1.9.1"
+description = "Astropy affiliated package for accessing Virtual Observatory data and services"
 optional = false
-python-versions = "*"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "pytz-2025.2-py2.py3-none-any.whl", hash = "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"},
-    {file = "pytz-2025.2.tar.gz", hash = "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3"},
+    {file = "pyvo-1.9.1-py3-none-any.whl", hash = "sha256:098648d00943440f56c1d00ac76330433578a0725ea6187afaa6bae08545bb53"},
+    {file = "pyvo-1.9.1.tar.gz", hash = "sha256:2f26c99af7c32f3c34b919e2d14eaf1a95914176d693fb7769773f3ab0b7999d"},
+]
+
+[package.dependencies]
+astropy = ">=5.0"
+requests = "*"
+
+[package.extras]
+all = ["defusedxml", "pillow"]
+docs = ["sphinx-astropy"]
+test = ["pytest-astropy (>=0.6)", "pytest-doctestplus (>=0.13)", "pytest-timeout", "requests-mock"]
+
+[[package]]
+name = "pywin32-ctypes"
+version = "0.2.3"
+description = "A (partial) reimplementation of pywin32 using ctypes/cffi"
+optional = false
+python-versions = ">=3.6"
+groups = ["main"]
+markers = "sys_platform == \"win32\""
+files = [
+    {file = "pywin32-ctypes-0.2.3.tar.gz", hash = "sha256:d162dc04946d704503b2edc4d55f3dba5c1d539ead017afa00142c38b9885755"},
+    {file = "pywin32_ctypes-0.2.3-py3-none-any.whl", hash = "sha256:8a1513379d709975552d202d942d9837758905c8d01eb82b8bcc30918929e7b8"},
 ]
 
 [[package]]
@@ -2410,6 +2549,7 @@ version = "6.0.3"
 description = "YAML parser and emitter for Python"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"},
     {file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"},
@@ -2492,6 +2632,7 @@ version = "0.2.0"
 description = "CFFI bindings to librabbitmq 0.8.0"
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "rabbitmq-0.2.0-py2.py3-none-any.whl", hash = "sha256:74cfbc0f365019a8f318cd1ad9b1f85682768568fa3816740df37509a6dfbc2d"},
 ]
@@ -2505,6 +2646,7 @@ version = "5.3.1"
 description = "Python client for Redis database and key-value store"
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
     {file = "redis-5.3.1-py3-none-any.whl", hash = "sha256:dc1909bd24669cc31b5f67a039700b16ec30571096c5f1f0d9d2324bff31af97"},
     {file = "redis-5.3.1.tar.gz", hash = "sha256:ca49577a531ea64039b5a36db3d6cd1a0c7a60c34124d46924a45b956e8cf14c"},
@@ -2524,6 +2666,7 @@ version = "0.19.0"
 description = "Reproject astronomical images"
 optional = false
 python-versions = ">=3.11"
+groups = ["main"]
 files = [
     {file = "reproject-0.19.0-cp311-abi3-macosx_10_9_x86_64.whl", hash = "sha256:67cc917218bad54b95b1a871787b347caa42291e052f66c7b7a173c065d9c6ad"},
     {file = "reproject-0.19.0-cp311-abi3-macosx_11_0_arm64.whl", hash = "sha256:60f49baabeee8c5dc8ef3a98335bc014b724c38e716a3108337dfd093aa41190"},
@@ -2553,24 +2696,25 @@ testall = ["asdf", "gwcs", "pyvo", "shapely (>=2.0.2)", "sunpy[map] (>=6.0.1)"]
 
 [[package]]
 name = "requests"
-version = "2.32.5"
+version = "2.34.2"
 description = "Python HTTP for Humans."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "requests-2.32.5-py3-none-any.whl", hash = "sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6"},
-    {file = "requests-2.32.5.tar.gz", hash = "sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf"},
+    {file = "requests-2.34.2-py3-none-any.whl", hash = "sha256:2a0d60c172f83ac6ab31e4554906c0f3b3588d37b5cb939b1c061f4907e278e0"},
+    {file = "requests-2.34.2.tar.gz", hash = "sha256:f288924cae4e29463698d6d60bc6a4da69c89185ad1e0bcc4104f584e960b9ed"},
 ]
 
 [package.dependencies]
-certifi = ">=2017.4.17"
+certifi = ">=2023.5.7"
 charset_normalizer = ">=2,<4"
 idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<3"
+urllib3 = ">=1.26,<3"
 
 [package.extras]
 socks = ["PySocks (>=1.5.6,!=1.5.7)"]
-use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
+use-chardet-on-py3 = ["chardet (>=3.0.2,<8)"]
 
 [[package]]
 name = "retrying"
@@ -2578,6 +2722,7 @@ version = "1.4.2"
 description = "Retrying"
 optional = false
 python-versions = ">=3.6"
+groups = ["main"]
 files = [
     {file = "retrying-1.4.2-py3-none-any.whl", hash = "sha256:bbc004aeb542a74f3569aeddf42a2516efefcdaff90df0eb38fbfbf19f179f59"},
     {file = "retrying-1.4.2.tar.gz", hash = "sha256:d102e75d53d8d30b88562d45361d6c6c934da06fab31bd81c0420acb97a8ba39"},
@@ -2585,20 +2730,21 @@ files = [
 
 [[package]]
 name = "s3transfer"
-version = "0.16.0"
+version = "0.19.1"
 description = "An Amazon S3 Transfer Manager"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
 files = [
-    {file = "s3transfer-0.16.0-py3-none-any.whl", hash = "sha256:18e25d66fed509e3868dc1572b3f427ff947dd2c56f844a5bf09481ad3f3b2fe"},
-    {file = "s3transfer-0.16.0.tar.gz", hash = "sha256:8e990f13268025792229cd52fa10cb7163744bf56e719e0b9cb925ab79abf920"},
+    {file = "s3transfer-0.19.1-py3-none-any.whl", hash = "sha256:d5fd7005ee39307455ad5f310b5ea67f4b1960d7fed5b3671ee50c249de675de"},
+    {file = "s3transfer-0.19.1.tar.gz", hash = "sha256:d3d6371dc3f1e5c5427b2b457bcf13bcf87bec334c95aed18642eae61f6926f3"},
 ]
 
 [package.dependencies]
-botocore = ">=1.37.4,<2.0a.0"
+botocore = ">=1.37.4,<2.0a0"
 
 [package.extras]
-crt = ["botocore[crt] (>=1.37.4,<2.0a.0)"]
+crt = ["botocore[crt] (>=1.37.4,<2.0a0)"]
 
 [[package]]
 name = "scikit-image"
@@ -2606,6 +2752,7 @@ version = "0.23.2"
 description = "Image processing in Python"
 optional = false
 python-versions = ">=3.10"
+groups = ["main"]
 files = [
     {file = "scikit_image-0.23.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f9a8db6c52f8d0e1474ea8320d7b8db442b4d6baa29dd0acbd02f8a49572f18a"},
     {file = "scikit_image-0.23.2-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:524b51a7440e46ed2ebbde7bc288bf2dde1dee2caafdd9513b2aca38a48223b7"},
@@ -2638,73 +2785,106 @@ tifffile = ">=2022.8.12"
 [package.extras]
 build = ["Cython (>=3.0.4)", "build", "meson-python (>=0.15)", "ninja", "numpy (>=2.0.0rc1)", "packaging (>=21)", "pythran", "setuptools (>=67)", "spin (==0.8)", "wheel"]
 data = ["pooch (>=1.6.0)"]
-developer = ["ipython", "pre-commit", "tomli"]
+developer = ["ipython", "pre-commit", "tomli ; python_version < \"3.11\""]
 docs = ["PyWavelets (>=1.1.1)", "dask[array] (>=2022.9.2)", "ipykernel", "ipywidgets", "kaleido", "matplotlib (>=3.6)", "myst-parser", "numpydoc (>=1.7)", "pandas (>=1.5)", "plotly (>=5.10)", "pooch (>=1.6)", "pydata-sphinx-theme (>=0.15.2)", "pytest-doctestplus", "pytest-runner", "scikit-learn (>=1.1)", "seaborn (>=0.11)", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-gallery (>=0.14)", "sphinx_design (>=0.5)", "tifffile (>=2022.8.12)"]
 optional = ["PyWavelets (>=1.1.1)", "SimpleITK", "astropy (>=5.0)", "cloudpickle (>=0.2.1)", "dask[array] (>=2021.1.0)", "matplotlib (>=3.6)", "pooch (>=1.6.0)", "pyamg", "scikit-learn (>=1.1)"]
 test = ["asv", "numpydoc (>=1.7)", "pooch (>=1.6.0)", "pytest (>=7.0)", "pytest-cov (>=2.11.0)", "pytest-doctestplus", "pytest-faulthandler", "pytest-localserver"]
 
 [[package]]
 name = "scipy"
-version = "1.15.3"
+version = "1.17.1"
 description = "Fundamental algorithms for scientific computing in Python"
 optional = false
-python-versions = ">=3.10"
-files = [
-    {file = "scipy-1.15.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:a345928c86d535060c9c2b25e71e87c39ab2f22fc96e9636bd74d1dbf9de448c"},
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 ]
 
 [package.dependencies]
-numpy = ">=1.23.5,<2.5"
+numpy = ">=1.26.4,<2.7"
 
 [package.extras]
-dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
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+dev = ["click (<8.3.0)", "cython-lint (>=0.12.2)", "mypy (==1.10.0)", "pycodestyle", "ruff (>=0.12.0)", "spin", "types-psutil", "typing_extensions"]
+doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.19.1)", "jupytext", "linkify-it-py", "matplotlib (>=3.5)", "myst-nb (>=1.2.0)", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.2.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)", "tabulate"]
+test = ["Cython", "array-api-strict (>=2.3.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest (>=8.0.0)", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
+[[package]]
+name = "secretstorage"
+version = "3.5.0"
+description = "Python bindings to FreeDesktop.org Secret Service API"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "sys_platform == \"linux\""
+files = [
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+]
+
+[package.dependencies]
+cryptography = ">=2.0"
+jeepney = ">=0.6"
 
 [[package]]
 name = "six"
@@ -2712,6 +2892,7 @@ version = "1.17.0"
 description = "Python 2 and 3 compatibility utilities"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main", "test"]
 files = [
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@@ -2723,17 +2904,31 @@ version = "1.1.0"
 description = "A lazy-loading, fancy-sliceable iterable."
 optional = false
 python-versions = "*"
+groups = ["main"]
 files = [
     {file = "slicerator-1.1.0-py3-none-any.whl", hash = "sha256:167668d48c6d3a5ba0bd3d54b2688e81ee267dc20aef299e547d711e6f3c441a"},
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 ]
 
+[[package]]
+name = "soupsieve"
+version = "2.8.4"
+description = "A modern CSS selector implementation for Beautiful Soup."
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "soupsieve-2.8.4.tar.gz", hash = "sha256:e121fd02e975c695e4e9e8774a5ee35d74714b59307868dcc5319ad2d9e3328e"},
+]
+
 [[package]]
 name = "sqlparse"
 version = "0.5.5"
 description = "A non-validating SQL parser."
 optional = false
 python-versions = ">=3.8"
+groups = ["main"]
 files = [
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@@ -2745,25 +2940,26 @@ doc = ["sphinx"]
 
 [[package]]
 name = "tifffile"
-version = "2025.5.10"
+version = "2026.3.3"
 description = "Read and write TIFF files"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.11"
+groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
 numpy = "*"
 
 [package.extras]
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-codecs = ["imagecodecs (>=2024.12.30)"]
+all = ["defusedxml", "fsspec", "imagecodecs (>=2025.11.11)", "kerchunk", "lxml", "matplotlib", "zarr (>=3.1.5)"]
+codecs = ["imagecodecs (>=2025.11.11)"]
 plot = ["matplotlib"]
-test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "lfdfiles", "lxml", "ndtiff", "oiffile", "psdtags", "pytest", "roifile", "xarray", "zarr (<3)"]
+test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "kerchunk", "lfdfiles", "lxml", "ndtiff", "oiffile", "psdtags", "pytest", "requests", "roifile", "xarray", "zarr (>=3.1.5)"]
 xml = ["defusedxml", "lxml"]
-zarr = ["fsspec", "zarr (<3)"]
+zarr = ["fsspec", "kerchunk", "zarr (>=3.1.5)"]
 
 [[package]]
 name = "toolz"
@@ -2771,6 +2967,7 @@ version = "1.1.0"
 description = "List processing tools and functional utilities"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
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@@ -2778,180 +2975,171 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.15.0"
+version = "4.16.0"
 description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
 python-versions = ">=3.9"
+groups = ["main"]
 files = [
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 ]
 
 [[package]]
 name = "tzdata"
-version = "2025.3"
+version = "2026.3"
 description = "Provider of IANA time zone data"
 optional = false
 python-versions = ">=2"
+groups = ["main"]
+markers = "sys_platform == \"win32\""
 files = [
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 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
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+python-versions = ">=3.10"
+groups = ["main"]
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 [package.extras]
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-zstd = ["backports-zstd (>=1.0.0)"]
+zstd = ["backports-zstd (>=1.0.0) ; python_version < \"3.14\""]
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
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+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
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+    {file = "yarl-1.24.2-cp314-cp314t-win_amd64.whl", hash = "sha256:a7624b1ca46ca5d7b864ef0d2f8efe3091454085ee1855b4e992314529972215"},
+    {file = "yarl-1.24.2-cp314-cp314t-win_arm64.whl", hash = "sha256:e434a45ce2e7a947f951fc5a8944c8cc080b7e59f9c50ae80fd39107cf88126d"},
+    {file = "yarl-1.24.2-py3-none-any.whl", hash = "sha256:2783d9226db8797636cd6896e4de81feed252d1db72265686c9558d97a4d94b9"},
+    {file = "yarl-1.24.2.tar.gz", hash = "sha256:9ac374123c6fd7abf64d1fec93962b0bd4ee2c19751755a762a72dd96c0378f8"},
 ]
 
 [package.dependencies]
@@ -2965,6 +3153,7 @@ version = "3.1.5"
 description = "An implementation of chunked, compressed, N-dimensional arrays for Python"
 optional = false
 python-versions = ">=3.11"
+groups = ["main"]
 files = [
     {file = "zarr-3.1.5-py3-none-any.whl", hash = "sha256:29cd905afb6235b94c09decda4258c888fcb79bb6c862ef7c0b8fe009b5c8563"},
     {file = "zarr-3.1.5.tar.gz", hash = "sha256:fbe0c79675a40c996de7ca08e80a1c0a20537bd4a9f43418b6d101395c0bba2b"},
@@ -2989,24 +3178,26 @@ test = ["coverage (>=7.10)", "hypothesis", "mypy", "numpydoc", "packaging", "pyt
 
 [[package]]
 name = "zipp"
-version = "3.23.0"
+version = "4.1.0"
 description = "Backport of pathlib-compatible object wrapper for zip files"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
+groups = ["main"]
+markers = "python_version == \"3.11\""
 files = [
-    {file = "zipp-3.23.0-py3-none-any.whl", hash = "sha256:071652d6115ed432f5ce1d34c336c0adfd6a884660d1e9712a256d3d3bd4b14e"},
-    {file = "zipp-3.23.0.tar.gz", hash = "sha256:a07157588a12518c9d4034df3fbbee09c814741a33ff63c05fa29d26a2404166"},
+    {file = "zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f"},
+    {file = "zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602"},
 ]
 
 [package.extras]
-check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"]
+check = ["pytest-checkdocs (>=2.14)", "pytest-ruff (>=0.2.1) ; sys_platform != \"cygwin\""]
 cover = ["pytest-cov"]
 doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-enabler = ["pytest-enabler (>=2.2)"]
+enabler = ["pytest-enabler (>=3.4)"]
 test = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more_itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"]
-type = ["pytest-mypy"]
+type = ["pytest-mypy (>=1.0.1) ; platform_python_implementation != \"PyPy\""]
 
 [metadata]
-lock-version = "2.0"
+lock-version = "2.1"
 python-versions = ">=3.11,<4.0"
-content-hash = "94c08daf8436c44842dc1d78517c11596673dc69b569cabee5fec298616daa9d"
+content-hash = "ee69558a4ebd0529427259500a1a5dff9c859ebabbec7f6e0e2add32858f73f1"
diff --git a/pyproject.toml b/pyproject.toml
index 9421eb0..f8f2e29 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,6 +34,7 @@ aiohttp = "^3.12.2"
 retrying = "^1.4.2"
 dramatiq = "<2.0"
 reproject = "^0.19.0"
+astroquery = "^0.4.7"
 
 [tool.poetry.group.test.dependencies]
 pytest = "^7.4.3"