--- dataset_info: features: - name: object_id dtype: int64 - name: targetid dtype: int64 - name: redshift dtype: float64 - name: RGB_image dtype: image - name: VIS_image sequence: sequence: float32 - name: NISP_Y_image sequence: sequence: float32 - name: NISP_J_image sequence: sequence: float32 - name: NISP_H_image sequence: sequence: float32 - name: sed_data struct: - name: flux_g_ext_decam_1fwhm_aper dtype: float64 - name: flux_g_ext_hsc_1fwhm_aper dtype: float64 - name: flux_h_1fwhm_aper dtype: float64 - name: flux_i_ext_decam_1fwhm_aper dtype: float64 - name: flux_i_ext_panstarrs_1fwhm_aper dtype: float64 - name: flux_j_1fwhm_aper dtype: float64 - name: flux_r_ext_decam_1fwhm_aper dtype: float64 - name: flux_r_ext_megacam_1fwhm_aper dtype: float64 - name: flux_u_ext_megacam_1fwhm_aper dtype: float64 - name: flux_vis_1fwhm_aper dtype: float64 - name: flux_y_1fwhm_aper dtype: float64 - name: flux_z_ext_decam_1fwhm_aper dtype: float64 - name: flux_z_ext_hsc_1fwhm_aper dtype: float64 - name: spectrum struct: - name: error sequence: float64 - name: flux sequence: float64 - name: wavelength sequence: float64 splits: - name: train_batch_1 num_bytes: 555053103.0 num_examples: 500 - name: train_batch_2 num_bytes: 555136531.0 num_examples: 500 - name: train_batch_3 num_bytes: 555149122.0 num_examples: 500 - name: train_batch_4 num_bytes: 554924597.0 num_examples: 500 - name: train_batch_5 num_bytes: 555203134.0 num_examples: 500 - name: train_batch_6 num_bytes: 555025037.0 num_examples: 500 - name: train_batch_7 num_bytes: 555030892.0 num_examples: 500 - name: train_batch_8 num_bytes: 554836654.0 num_examples: 500 - name: train_batch_9 num_bytes: 555019798.0 num_examples: 500 - name: train_batch_10 num_bytes: 555077290.0 num_examples: 500 - name: train_batch_11 num_bytes: 555001931.0 num_examples: 500 - name: train_batch_12 num_bytes: 555099341.0 num_examples: 500 - name: train_batch_13 num_bytes: 555272934.0 num_examples: 500 - name: train_batch_14 num_bytes: 554902040.0 num_examples: 500 - name: train_batch_15 num_bytes: 554830908.0 num_examples: 500 - name: train_batch_16 num_bytes: 555106227.0 num_examples: 500 - name: train_batch_17 num_bytes: 555088571.0 num_examples: 500 - name: train_batch_18 num_bytes: 555102580.0 num_examples: 500 - name: train_batch_19 num_bytes: 555014485.0 num_examples: 500 - name: train_batch_20 num_bytes: 555155880.0 num_examples: 500 - name: train_batch_21 num_bytes: 555046414.0 num_examples: 500 - name: train_batch_22 num_bytes: 554897495.0 num_examples: 500 - name: train_batch_23 num_bytes: 554936135.0 num_examples: 500 - name: train_batch_24 num_bytes: 555038397.0 num_examples: 500 - name: train_batch_25 num_bytes: 232958879.0 num_examples: 210 - name: test_batch_1 num_bytes: 555139471.0 num_examples: 500 - name: test_batch_2 num_bytes: 555092924.0 num_examples: 500 - name: test_batch_3 num_bytes: 555168429.0 num_examples: 500 - name: test_batch_4 num_bytes: 554973812.0 num_examples: 500 - name: test_batch_5 num_bytes: 555121067.0 num_examples: 500 - name: test_batch_6 num_bytes: 555066283.0 num_examples: 500 - name: test_batch_7 num_bytes: 58807273.0 num_examples: 53 download_size: 16358854040 dataset_size: 16943277634.0 configs: - config_name: default data_files: - split: train_batch_1 path: data/train_batch_1-* - split: train_batch_2 path: data/train_batch_2-* - split: train_batch_3 path: data/train_batch_3-* - split: train_batch_4 path: data/train_batch_4-* - split: train_batch_5 path: data/train_batch_5-* - split: train_batch_6 path: data/train_batch_6-* - split: train_batch_7 path: data/train_batch_7-* - split: train_batch_8 path: data/train_batch_8-* - split: train_batch_9 path: data/train_batch_9-* - split: train_batch_10 path: data/train_batch_10-* - split: train_batch_11 path: data/train_batch_11-* - split: train_batch_12 path: data/train_batch_12-* - split: train_batch_13 path: data/train_batch_13-* - split: train_batch_14 path: data/train_batch_14-* - split: train_batch_15 path: data/train_batch_15-* - split: train_batch_16 path: data/train_batch_16-* - split: train_batch_17 path: data/train_batch_17-* - split: train_batch_18 path: data/train_batch_18-* - split: train_batch_19 path: data/train_batch_19-* - split: train_batch_20 path: data/train_batch_20-* - split: train_batch_21 path: data/train_batch_21-* - split: train_batch_22 path: data/train_batch_22-* - split: train_batch_23 path: data/train_batch_23-* - split: train_batch_24 path: data/train_batch_24-* - split: train_batch_25 path: data/train_batch_25-* - split: test_batch_1 path: data/test_batch_1-* - split: test_batch_2 path: data/test_batch_2-* - split: test_batch_3 path: data/test_batch_3-* - split: test_batch_4 path: data/test_batch_4-* - split: test_batch_5 path: data/test_batch_5-* - split: test_batch_6 path: data/test_batch_6-* - split: test_batch_7 path: data/test_batch_7-* --- # DESI EDR × Euclid AstroPT Q1 Dataset ## Dataset Description This dataset contains the cross-matched catalog between **DESI Early Data Release (EDR)** and sample of **Quick Data Release 1 (Q1)** data from Euclid Consortium: Siudek et al. 2025 ([arXiv:2503.15312](https://ui.adsabs.harvard.edu/abs/2025arXiv250315312E/abstract)). ### Dataset Summary - **Total Sources**: 15,263 galaxies - **Cross-match Radius**: 0.5 arcsec - **Data Products**: - Euclid VIS imaging - Euclid NISP imaging (Y, J, H bands) - DESI spectra with spectroscopic redshifts - metadata: catalog of DESI logM and Z values. For more details, see [Siudek et al. 2024](https://ui.adsabs.harvard.edu/abs/2024A%26A...691A.308S/abstract) and [Siudek et al. 2025b](https://ui.adsabs.harvard.edu/abs/2025A%26A...700A.209S/abstract). The metadata catalog is available at https://huggingface.co/datasets/msiudek/astroPT_euclid_metadata (e.g., select entries where DESI_TARGETID is not null). ## Source Data ### DESI Sample Selection Applied quality cuts to DESI EDR: - `ZCAT_PRIMARY == True` - `ZWARN == 0` or `ZWARN == 4` - `COADD_FIBERSTATUS == 0` - `SPECTYPE != 'STAR'` - `Z > 0` ### Euclid Sample Selection From Euclid Consortium: Siudek et al. 2025 ([arXiv:2503.15312](https://ui.adsabs.harvard.edu/abs/2025arXiv250315312E/abstract)). ## Usage ```python from datasets import load_dataset dataset = load_dataset("msiudek/astroPT_euclid_desi_dataset") ``` ## Citation If you use this dataset, please cite: **Siudek et al. 2025** ```bibtex @ARTICLE{2025arXiv250315312S, author = {{Siudek}, M. and collaborators}, title = "{Euclid Consortium Paper Title}", journal = {arXiv e-prints}, year = 2025, month = mar, eid = {arXiv:2503.15312}, pages = {arXiv:2503.15312}, archivePrefix = {arXiv}, eprint = {2503.15312}, } @dataset{euclid_desi_edr_q1, author = {Siudek, M. and collaborators}, title = {DESI EDR × Euclid Q1 Cross-matched Dataset}, year = {2025}, publisher = {Hugging Face}, url = {https://huggingface.co/datasets/msiudek/astroPT_euclid_desi_dataset} } ```