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Datasets:
bamfai
/
galaxy-chirality-catalog

Tasks:
Image Classification
Tabular Classification
Modalities:
Tabular
Text
Formats:
parquet
Size:
1M - 10M
Tags:
astronomy
galaxies
chirality
cosmology
parity-violation
equivariant
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
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Dataset Viewer
Auto-converted to Parquet Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
dr8_id: large_string
p_cw_raw_x: double
p_ccw_raw_x: double
p_ns_raw_x: double
p_cw_eq: double
p_ccw_eq: double
p_ns_eq: double
class_raw_x: large_string
class_eq: large_string
confidence_eq: double
ra: double
dec: double
p_cw_raw_y: double
p_ccw_raw_y: double
p_ns_raw_y: double
class_raw_y: large_string
confidence_raw: double
image_url: large_string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2395
to
{'dr8_id': Value('int64'), 'p_cw_eq': Value('float32'), 'p_ccw_eq': Value('float32'), 'p_ns_eq': Value('float32'), 'class_eq': Value('string'), 'confidence_eq': Value('float32'), 'ra': Value('float64'), 'dec': Value('float64'), 'p_cw_raw': Value('float32'), 'p_ccw_raw': Value('float32'), 'p_ns_raw': Value('float32'), 'class_raw': Value('string'), 'confidence_raw': Value('float32'), 'image_url': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 209, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 147, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              dr8_id: large_string
              p_cw_raw_x: double
              p_ccw_raw_x: double
              p_ns_raw_x: double
              p_cw_eq: double
              p_ccw_eq: double
              p_ns_eq: double
              class_raw_x: large_string
              class_eq: large_string
              confidence_eq: double
              ra: double
              dec: double
              p_cw_raw_y: double
              p_ccw_raw_y: double
              p_ns_raw_y: double
              class_raw_y: large_string
              confidence_raw: double
              image_url: large_string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2395
              to
              {'dr8_id': Value('int64'), 'p_cw_eq': Value('float32'), 'p_ccw_eq': Value('float32'), 'p_ns_eq': Value('float32'), 'class_eq': Value('string'), 'confidence_eq': Value('float32'), 'ra': Value('float64'), 'dec': Value('float64'), 'p_cw_raw': Value('float32'), 'p_ccw_raw': Value('float32'), 'p_ns_raw': Value('float32'), 'class_raw': Value('string'), 'confidence_raw': Value('float32'), 'image_url': Value('string')}
              because column names don't match

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Galaxy Chirality Catalog — 8.47M Galaxies

A production catalog of 8,474,531 galaxies from the DESI Legacy Survey DR8, classified by visual chirality (spin direction) into three classes: clockwise (CW), counter-clockwise (CCW), and not spiral (NOT_SPIRAL).

This catalog was produced using test-time equivariant averaging to eliminate all optical and handedness biases from the classifier. All 8 of 8 bias-validation tests pass, confirming that the catalog is free of systematic chirality bias.

Key Results

Metric Value
Total galaxies 8,474,531
CW galaxies 1,592,107
CCW galaxies 1,609,053
Not-spiral galaxies 5,273,371
CW / (CW + CCW) 0.4974
Dipole amplitude 0.0044
Dipole significance 0.43σ (null)
Dipole p-value 0.30
Dipole direction (l, b) (293.0°, 12.0°)

The equivariant CW fraction of 0.4974 is consistent with parity symmetry. The dipole search over high-confidence spirals (n = 949,584) yields an amplitude of 0.43σ — fully consistent with isotropy and no preferred cosmic spin axis.

Column Descriptions

Column Type Description
dr8_id int64 Unique galaxy identifier from DESI Legacy Survey DR8
p_cw_eq float32 Equivariant probability of clockwise spiral
p_ccw_eq float32 Equivariant probability of counter-clockwise spiral
p_ns_eq float32 Equivariant probability of not-spiral
class_eq string Equivariant predicted class (CW / CCW / NOT_SPIRAL)
confidence_eq float32 Equivariant classification confidence (max probability)
ra float64 Right ascension (degrees, J2000)
dec float64 Declination (degrees, J2000)
p_cw_raw float32 Raw (single-pass) probability of clockwise spiral
p_ccw_raw float32 Raw (single-pass) probability of counter-clockwise spiral
p_ns_raw float32 Raw (single-pass) probability of not-spiral
class_raw string Raw predicted class (CW / CCW / NOT_SPIRAL)
confidence_raw float32 Raw classification confidence
image_url string URL to the galaxy cutout image in DESI Legacy Survey

Equivariant vs. Raw Predictions

  • Equivariant (_eq): Test-time averaged over all 8 dihedral-group transformations (4 rotations x 2 reflections). This eliminates any optical handedness bias from the classifier. Use these columns for science.
  • Raw (_raw): Single forward-pass predictions without augmentation. Included for comparison and bias-validation purposes.

Methodology

  1. Source images: 8.47M galaxy cutouts from Smith42/galaxies (DESI Legacy Survey DR8).
  2. Model: bamfai/galaxy-chirality-v2 — a 3-class ResNet-based classifier trained on the GalaxyMNIST morphology dataset with chirality labels.
  3. Equivariant averaging: Each galaxy image is transformed under all 8 elements of the dihedral group D4. CW/CCW probabilities are swapped for reflections. The 8 probability vectors are averaged to produce perfectly equivariant predictions.
  4. Bias validation: 8/8 tests pass, including CW/CCW symmetry, hemisphere balance, magnitude independence, and equivariant consistency checks.
  5. Dipole search: Spherical harmonic decomposition of the CW excess field over high-confidence spirals, testing for a preferred cosmic axis.

Usage 

from datasets import load_dataset

ds = load_dataset("bamfai/galaxy-chirality-catalog")

# Access the catalog
df = ds["train"].to_pandas()

# High-confidence spirals only
spirals = df[df["class_eq"].isin(["CW", "CCW"]) & (df["confidence_eq"] > 0.7)]
print(f"CW fraction: {(spirals.class_eq == CW).mean():.4f}")

Files

File Description
catalog_production.parquet Full 8.47M galaxy catalog (909 MB)
catalog_c_summary.json Summary statistics (counts, fractions, runtime)
dipole_catalog_c.json Dipole analysis results (amplitude, direction, significance)

Citation 

@dataset{golden2026chirality,
  author    = {Houston Golden},
  title     = {Galaxy Chirality Catalog: 8.47M Galaxies from DESI Legacy Survey DR8},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/bamfai/galaxy-chirality-catalog},
  note      = {BigBounce Research, https://bigbounce.hubify.app}
}

License

CC-BY-4.0

Links

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Number of rows:
8,474,531
Total file size:
952 MB