--- license: cc-by-sa-4.0 pretty_name: galaxies_with_hats size_categories: - 1M Sky coverage and galaxy density map, train split # galaxy with hat Galaxies with HATS galaxy with hat This is a [HATS](https://hats.readthedocs.io) (HEALPix Adaptive Tiling Scheme) version of [Smith42/galaxies](https://huggingface.co/datasets/Smith42/galaxies) (revision `v2.0`): ~8.5 million 256×256 pixel PNG galaxy cutouts from the [DESI Legacy Survey DR8](https://www.legacysurvey.org/dr8/description/), centred on the galaxy source, spatially partitioned on the sky, and bundled with all ~160 metadata columns (Galaxy Zoo DESI morphologies, NSA photometry, redshifts, and OSSY / ALFALFA / JHU-MPA cross-matches) in every row. Where the original dataset is ordered for ML training, this edition is ordered for sky-based analysis: every row carries `ra`/`dec` (degrees) and a `_healpix_29` spatial index, and rows are partitioned into HEALPix tiles so that cone / box / cross-match queries touch only the relevant files. The original 98/1/1 train/validation/test splits are preserved as three separate HATS collections: | Collection | Rows | HEALPix partitions | Size | | --- | --- | --- | --- | | [`train`](train/) | 8,474,566 | 1,555 | ~709 GB | | [`validation`](validation/) | 86,499 | 27 | ~7.3 GB | | [`test`](test/) | 86,471 | 27 | ~7.3 GB | ## Stream it with 🤗 Datasets HATS is plain Parquet, so the usual Hugging Face stack works out of the box (see [Multimodal Universe × HATS](https://huggingface.co/blog/hugging-science/multimodal-universe-hats)): ```python from datasets import load_dataset galaxies = load_dataset("Smith42/galaxies_with_hats", split="train", streaming=True) example = next(iter(galaxies)) example["image"] # -> PIL image, decoded automatically ``` Unlike the original ML-ordered repo, streaming order here follows the HEALPix sky tiling, meaning that consecutive examples are sky neighbours. Great for spatially coherent browsing; if you want i.i.d.-ish batches for training, add `.shuffle(buffer_size=10_000)` or stream the original [Smith42/galaxies](https://huggingface.co/datasets/Smith42/galaxies) instead. ## Sky queries with LSDB We recommend [LSDB](https://lsdb.io) (`pip install lsdb`) for spatial work with HATS catalogs: ```python import lsdb # Open a split (lazy -- nothing is downloaded yet) galaxies = lsdb.open_catalog("hf://datasets/Smith42/galaxies_hats/train") # One-degree cone around (RA, Dec) = (161.0, 48.0) cone = lsdb.open_catalog( "hf://datasets/Smith42/galaxies_hats/train", search_filter=lsdb.ConeSearch(ra=161.0, dec=48.0, radius_arcsec=3600.0), ) df = cone.compute() # materialise to pandas; df["image"]["bytes"] holds the JPGs # Cross-match your own catalog against the galaxy images # my_cat = lsdb.from_dataframe(my_dataframe, ra_column="ra", dec_column="dec") # matched = my_cat.crossmatch(galaxies, radius_arcsec=1.0) ``` Each catalog is also a plain [Apache Parquet dataset](https://arrow.apache.org/docs/python/dataset.html) (`//dataset/`), directly readable with `pandas`, `pyarrow`, `dask`, `Spark`, or `DuckDB` — partitions are laid out as `Norder={order}/Dir={dir}/Npix={pixel}.parquet` HEALPix tiles of ~10,000 rows (≈0.8 GB) each. ```python import pandas as pd # read a single sky tile tile = pd.read_parquet( "hf://datasets/Smith42/galaxies_hats/test/test/dataset/Norder=0/Dir=0/Npix=4.parquet" ) ``` ## Catalog columns All three collections share an identical 170-column schema: the original `image` (struct of PNG `bytes` + `path`, decoded as an image by 🤗 `datasets`), `dr8_id`, `galaxy_size`, `ra`, `dec`, and all metadata columns of `Smith42/galaxies` v2.0, plus the `_healpix_29` spatial index added by HATS. The main HATS catalog contains the following columns: | **Name** | **`_healpix_29`** | **`image`** | **`dr8_id`** | **`galaxy_size`** | **`ra`** | **`dec`** | **`brickid`** | **`objid`** | **`smooth-or-featured_smooth_fraction`** | **`smooth-or-featured_featured-or-disk_fraction`** | **`smooth-or-featured_artifact_fraction`** | **`disk-edge-on_yes_fraction`** | **`disk-edge-on_no_fraction`** | **`has-spiral-arms_yes_fraction`** | **`has-spiral-arms_no_fraction`** | **`bar_strong_fraction`** | **`bar_weak_fraction`** | **`bar_no_fraction`** | **`bulge-size_dominant_fraction`** | **`bulge-size_large_fraction`** | **`bulge-size_moderate_fraction`** | **`bulge-size_small_fraction`** | **`bulge-size_none_fraction`** | **`how-rounded_round_fraction`** | **`how-rounded_in-between_fraction`** | **`how-rounded_cigar-shaped_fraction`** | **`edge-on-bulge_boxy_fraction`** | **`edge-on-bulge_none_fraction`** | **`edge-on-bulge_rounded_fraction`** | **`spiral-winding_tight_fraction`** | **`spiral-winding_medium_fraction`** | **`spiral-winding_loose_fraction`** | **`spiral-arm-count_1_fraction`** | **`spiral-arm-count_2_fraction`** | **`spiral-arm-count_3_fraction`** | **`spiral-arm-count_4_fraction`** | **`spiral-arm-count_more-than-4_fraction`** | **`spiral-arm-count_cant-tell_fraction`** | **`merging_none_fraction`** | **`merging_minor-disturbance_fraction`** | **`merging_major-disturbance_fraction`** | **`merging_merger_fraction`** | **`file_name`** | **`iauname`** | **`est_petro_th50`** | **`mag_r_desi`** | **`mag_g_desi`** | **`mag_z_desi`** | **`petro_theta`** | **`petro_th50`** | **`petro_th90`** | **`petro_phi50`** | **`petro_phi90`** | **`petro_ba50`** | **`petro_ba90`** | **`elpetro_ba`** | **`elpetro_phi`** | **`elpetro_flux_r`** | **`elpetro_theta_r`** | **`elpetro_mass`** | **`sersic_n`** | **`sersic_ba`** | **`sersic_phi`** | **`elpetro_absmag_f`** | **`sersic_nmgy_f`** | **`elpetro_absmag_n`** | **`sersic_nmgy_n`** | **`elpetro_absmag_u`** | **`sersic_nmgy_u`** | **`elpetro_absmag_g`** | **`sersic_nmgy_g`** | **`elpetro_absmag_r`** | **`sersic_nmgy_r`** | **`elpetro_absmag_i`** | **`sersic_nmgy_i`** | **`elpetro_absmag_z`** | **`sersic_nmgy_z`** | **`redshift_nsa`** | **`mag_f`** | **`mag_n`** | **`mag_u`** | **`mag_g`** | **`mag_r`** | **`mag_i`** | **`mag_z`** | **`u_minus_r`** | **`elpetro_mass_log`** | **`dr7objid_ossy`** | **`ra_ossy`** | **`dec_ossy`** | **`redshift_ossy`** | **`log_l_oiii`** | **`fwhm`** | **`e_fwhm`** | **`equiv_width`** | **`log_l_ha`** | **`log_m_bh`** | **`upper_e_log_m_bh`** | **`lower_e_log_m_bh`** | **`log_bolometric_l`** | **`ra_alf`** | **`dec_alf`** | **`W50`** | **`sigW`** | **`W20`** | **`HIflux`** | **`sigflux`** | **`SNR`** | **`RMS`** | **`Dist`** | **`sigDist`** | **`logMH`** | **`siglogMH`** | **`ra_jhu`** | **`dec_jhu`** | **`fibre_sfr_avg`** | **`fibre_sfr_entropy`** | **`fibre_sfr_median`** | **`fibre_sfr_mode`** | **`fibre_sfr_p16`** | **`fibre_sfr_p2p5`** | **`fibre_sfr_p84`** | **`fibre_sfr_p97p5`** | **`fibre_ssfr_avg`** | **`fibre_ssfr_entropy`** | **`fibre_ssfr_median`** | **`fibre_ssfr_mode`** | **`fibre_ssfr_p16`** | **`fibre_ssfr_p2p5`** | **`fibre_ssfr_p84`** | **`fibre_ssfr_p97p5`** | **`total_ssfr_avg`** | **`total_ssfr_entropy`** | **`total_ssfr_flag`** | **`total_ssfr_median`** | **`total_ssfr_mode`** | **`total_ssfr_p16`** | **`total_ssfr_p2p5`** | **`total_ssfr_p84`** | **`total_ssfr_p97p5`** | **`total_sfr_avg`** | **`total_sfr_entropy`** | **`total_sfr_flag`** | **`total_sfr_median`** | **`total_sfr_mode`** | **`total_sfr_p16`** | **`total_sfr_p2p5`** | **`total_sfr_p84`** | **`total_sfr_p97p5`** | **`photoz_id`** | **`ra_photoz`** | **`dec_photoz`** | **`photo_z`** | **`photo_zerr`** | **`spec_z`** | **`mag_abs_g_photoz`** | **`mag_abs_r_photoz`** | **`mag_abs_z_photoz`** | **`mass_inf_photoz`** | **`mass_med_photoz`** | **`mass_sup_photoz`** | **`sfr_inf_photoz`** | **`sfr_sup_photoz`** | **`ssfr_inf_photoz`** | **`ssfr_med_photoz`** | **`ssfr_sup_photoz`** | **`sky_separation_arcsec_from_photoz`** | **`redshift`** | **`est_petro_th50_kpc`** | **`__index_level_0__`** | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Data Type** | int64 | struct | string | int64 | double | double | int32 | int32 | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | string | string | double | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | float | double | double | float | float | float | float | float | float | float | double | float | float | float | float | float | float | float | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | double | int64 | | **Null count** | 0 | *N/A* | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7,585,145 (89.5%) | 7,585,145 (89.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 7,923,531 (93.5%) | 1,508,220 (17.8%) | 1,508,220 (17.8%) | 1,508,220 (17.8%) | 8,288,807 (97.81%) | 8,288,807 (97.81%) | 8,288,807 (97.81%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 8,116,934 (95.78%) | 0 | 0 | 0 | 0 | 0 | 7,882,536 (93.01%) | 0 | 0 | 14,543 (0.17%) | 17,007 (0.2%) | 7,882,536 (93.01%) | 7,882,536 (93.01%) | 7,882,536 (93.01%) | 7,882,571 (93.01%) | 7,882,570 (93.01%) | 7,882,571 (93.01%) | 7,882,570 (93.01%) | 7,882,570 (93.01%) | 7,882,570 (93.01%) | 7,882,536 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,536 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,537 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,536 (93.01%) | 7,882,542 (93.01%) | 7,882,540 (93.01%) | 7,882,536 (93.01%) | 7,934,716 (93.63%) | 7,902,017 (93.24%) | 7,882,672 (93.02%) | 7,882,572 (93.01%) | 7,882,571 (93.01%) | 7,882,596 (93.01%) | 7,882,795 (93.02%) | 7,882,579 (93.01%) | 7,882,542 (93.01%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,471,529 (99.96%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 8,446,786 (99.67%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,440 (90.88%) | 7,701,440 (90.88%) | 7,701,438 (90.88%) | 7,701,440 (90.88%) | 7,701,440 (90.88%) | 7,701,440 (90.88%) | 7,701,440 (90.88%) | 7,701,438 (90.88%) | 7,701,686 (90.88%) | 7,702,153 (90.89%) | 7,701,438 (90.88%) | 7,702,153 (90.89%) | 7,702,153 (90.89%) | 7,702,153 (90.89%) | 7,702,153 (90.89%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 7,701,438 (90.88%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 632,529 (7.46%) | 624,776 (7.37%) | 7,400,771 (87.33%) | 624,776 (7.37%) | 626,277 (7.39%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 624,776 (7.37%) | 620,954 (7.33%) | 620,954 (7.33%) | 0 | | **Example row** | 396125208651480696 | {'bytes': b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x01\x00\x00\x… | 594276_229 | 4010 | 179.5 | 52.64 | 594276 | 229 | 0.7793 | 0.1612 | 0.05948 | | | | | | | | | | | | | 0.09985 | 0.8888 | 0.01132 | | | | | | | | | | | | | 0.7796 | 0.1714 | 0.03812 | 0.01085 | data/594276_229.jpg | | 2.622 | 17.73 | 18.19 | 17.4 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 8.002e+15 | 179.5 | 52.64 | 0.07962 | 0.0192 | | -19.72 | -20.14 | -20.45 | 8.916 | 8.95 | 8.984 | 1.732 | 1.868 | -7.196 | -7.128 | -7.044 | 0 | 0.07962 | 3.943 | 7154494 | | **Minimum value** | 739458926660 | *N/A* | 100000_1081 | 1094 | 2.953e-05 | -65.9 | 28612 | 0 | 0.01351 | 0.01837 | 0.02748 | 0.01325 | 0.01119 | 0.02962 | 0.01014 | 0.01488 | 0.02545 | 0.02917 | 0.006299 | 0.008042 | 0.02487 | 0.017 | 0.006605 | 0.01036 | 0.01239 | 0.007499 | 0.01565 | 0.01061 | 0.02994 | 0.01303 | 0.02092 | 0.01085 | 0.009553 | 0.02966 | 0.006751 | 0.006136 | 0.006134 | 0.01004 | 0.03816 | 0.02309 | 0.01019 | 0.008846 | data/100000_1081.jpg | J000000.74-091320.2 | 0.8155 | 8.82 | -13.78 | -16.47 | -0 | -0 | -0 | 0.01331 | 0.007782 | -0.9724 | -0.9724 | -0.9724 | 0.007782 | -1.958e+05 | -9999 | -0 | 0.5 | 0.15 | 0.0003052 | -26.29 | -1.794e+15 | -26.32 | -1.832e+16 | -27.26 | -1.15e+04 | -28.27 | -2.413e+07 | -28.59 | -1.538e+07 | -28.64 | -3.35e+07 | -28.14 | -4.193e+07 | -0.003335 | -15.01 | -3.218 | 7.685 | 8.314 | 5.779 | 6.685 | 7.777 | -7.035 | -∞ | 5.877e+17 | 0.5123 | -10.96 | 0.01596 | -9999 | 1320 | 4.1 | 8.5 | 40.59 | 6.13 | -0 | -0 | -9.99 | 0.00333 | -0.2061 | 9 | 1 | -0 | -7.61 | 0.02 | 1.2 | 1.28 | 2.1 | 0.2 | 6.11 | 0.04 | 0.000686 | -11.25 | -5 | -6.917 | -∞ | -5 | -∞ | -∞ | -∞ | -∞ | -13.3 | -6.957 | -13.35 | -16 | -13.79 | -14.87 | -12.87 | -12.27 | -99 | -7.253 | -0 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -6.851 | -0 | -99 | -99 | -99 | -99 | -99 | -99 | 8e+15 | 2.953e-05 | -65.89 | 0.001 | 3.075e-05 | 0.0001144 | -32.36 | -32.48 | -33.13 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -99 | -0 | -0.003335 | -0.9113 | 0 | | **Maximum value** | 3458764069707388219 | *N/A* | 99999_704 | 249824 | 360 | 84.78 | 660728 | 31041 | 0.9204 | 0.9551 | 0.9179 | 0.9888 | 0.9868 | 0.9899 | 0.9704 | 0.8959 | 0.6597 | 0.9578 | 0.5212 | 0.6231 | 0.8273 | 0.9148 | 0.8917 | 0.9714 | 0.965 | 0.9764 | 0.6663 | 0.9517 | 0.9707 | 0.9373 | 0.731 | 0.9654 | 0.911 | 0.9506 | 0.8075 | 0.564 | 0.5158 | 0.7038 | 0.9512 | 0.5815 | 0.7444 | 0.8767 | data/99999_704.jpg | J235959.50+003645.4 | 20.16 | 19 | ∞ | ∞ | 254.8 | 154.4 | 163.4 | 180 | 180 | 1 | 1 | 1 | 180 | 6.528e+05 | 357.4 | 5.242e+12 | 6 | 1 | 180 | ∞ | 1.012e+15 | ∞ | 1.937e+10 | ∞ | 8.431e+05 | ∞ | 4.725e+05 | ∞ | 4.879e+06 | ∞ | 2.119e+06 | ∞ | 7.75e+05 | 0.15 | ∞ | ∞ | ∞ | ∞ | ∞ | ∞ | ∞ | 12.16 | 12.72 | 5.888e+17 | 359.8 | 68.5 | 0.1999 | 43.22 | 1.556e+04 | 4.29e+04 | 833.9 | 43.36 | 9.09 | 1.68 | 1.09 | 46.77 | 360 | 36.28 | 885 | 296 | 896 | 424.3 | 1.16 | 833 | 9.74 | 259.6 | 30.9 | 10.94 | 0.81 | 360 | 84.27 | 3.95 | 0 | 3.925 | 3.95 | 3.908 | 3.901 | 3.942 | 3.949 | -1 | 0 | -1 | -1 | -1 | -1 | -1 | -1 | -6.028 | 0 | 3 | -6.037 | -6 | -6.095 | -6.205 | -6.012 | -6.002 | 3.9 | 0 | 2 | 3.875 | 3.9 | 3.858 | 3.851 | 3.892 | 3.899 | 8.002e+15 | 360 | 84.77 | 0.5 | 0.2996 | 2.2 | 21.26 | -10.01 | 19.15 | 13.17 | 13.2 | 13.24 | 5.824 | 5.965 | -7.068 | -7 | -6.932 | 9.982 | 2.2 | 102.8 | 8689368 | Full per-collection details (file structure, partition lists, per-column tables) are in each collection's own README: [`train`](train/README.md), [`validation`](validation/README.md), [`test`](test/README.md). ## Provenance and fidelity - Source: [Smith42/galaxies](https://huggingface.co/datasets/Smith42/galaxies), revision `v2.0` (images and metadata pre-merged), CC-BY-SA-4.0. - Conversion: [hats-import](https://hats-import.readthedocs.io) v0.9.2 / [hats](https://hats.readthedocs.io) v0.9.2, partitioned on `ra`/`dec` with `pixel_threshold=10_000`, sorted by `dr8_id` within partitions. ## The original dataset Here we have ~8.5 million galaxy cutouts from the [DESI legacy survey DR8](https://www.legacysurvey.org/dr8/description/), centred on the galaxy source (256×256 pixel PNGs in the `v2.0` revision used here; the original `main` revision serves 512×512 JPGs). 1% of the images are split into a test set, and 1% into a validation set; the remaining 98% comprise the training set. For the ML-ordered original and AstroPT embeddings, see [Smith42/galaxies](https://huggingface.co/datasets/Smith42/galaxies) and [Smith42/galaxies_embeddings](https://huggingface.co/datasets/Smith42/galaxies_embeddings). ## Useful links Paper here: [https://arxiv.org/abs/2405.14930](https://arxiv.org/abs/2405.14930) Models here: [https://huggingface.co/Smith42/astroPT](https://huggingface.co/Smith42/astroPT) And here: [https://huggingface.co/Smith42/astroPT_v2.0](https://huggingface.co/Smith42/astroPT_v2.0) Code here: [https://github.com/smith42/astroPT](https://github.com/smith42/astroPT) Upstream catalogue is [on Zenodo](https://zenodo.org/records/8360385) and the paper describing the catalogue is available as [Walmsley+2023](https://doi.org/10.1093/mnras/stad2919). HATS and LSDB are developed by the [LINCC Frameworks](https://lsstdiscoveryalliance.org/programs/lincc-frameworks/) team — see [lsdb.io](https://docs.lsdb.io/en/stable/citation.html) for citation guidance. If you find this dataset useful please consider citing the sources below 🚀🚀: ``` @article{ref_dey2019, author = {Dey, A. and Schlegel, D. J. and Lang, D. and Blum, R. and Burleigh, K. and Fan, X. and Findlay, J. R. and Finkbeiner, D. and Herrera, D. and Juneau, S. and others}, title = {{Overview of the DESI Legacy Imaging Surveys}}, journal = {Astronomical Journal}, volume = {157}, number = {5}, pages = {168}, year = {2019}, issn = {1538-3881}, publisher = {The American Astronomical Society}, doi = {10.3847/1538-3881/ab089d} } ``` ``` @article{ref_walmsley2023, author = {Walmsley, M. and G{\ifmmode\acute{e}\else\'{e}\fi}ron, T. and Kruk, S. and Scaife, A. M. M. and Lintott, C. and Masters, K. L. and Dawson, J. M. and Dickinson, H. and Fortson, L. and Garland, I. L. and others}, title = {{Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys}}, journal = {Monthly Notices of the Royal Astronomical Society}, volume = {526}, number = {3}, pages = {4768--4786}, year = {2023}, issn = {0035-8711}, publisher = {Oxford Academic}, doi = {10.1093/mnras/stad2919} } ``` ``` @article{ref_smith2024, author = {Smith, M. J. and Roberts, R. J. and Angeloudi, E. and Huertas-Company, M.}, title = {{AstroPT: Scaling Large Observation Models for Astronomy}}, journal = {ArXiv e-prints}, year = {2024}, eprint = {2405.14930}, doi = {10.48550/arXiv.2405.14930} } ```