Jupyter Notebooks for Schochet & Planet et al. "200,000+ Deep Learning Inferred Periods of Stellar Variability from The All-Sky Automated Survey for Supernovae"

Notes:

To access reportable.csv and allgaiadata.parq, please access the Large Datafiles Zenodo associated with this work at https://doi.org/10.5281/zenodo.15848601.

To access the 'asassn_variables_x.csv' file, go to the ASAS-SN Variable Star page at https://asas-sn.osu.edu/variables and under Export g-Band Data tab click the Full Dataset and CSV Catalog button which takes you to a Google Drive where you can retrieve the file.

In the file for Figure 18, there is in-notebook crossmatching with Godoy-Rivera et al. (2025) . Specifically, after downloading their Table A1 off of Vizier at https://doi.org/10.26093/cds/vizier.36960243, the notebook directs you through some preliminary data preparation. You then upload it to the Gaia archive at https://gea.esac.esa.int/archive/ (the example shows this file uploaded as 'godoyrivera'). After that, you run the following ADQL query in the Advanced Search tab:

SELECT cross3.dr3_source_id, gsource.phot_g_mean_mag, gsource.phot_bp_mean_mag, gsource.phot_rp_mean_mag, mine.*, gsource.source_id AS dr2_id1, cross3.dr2_source_id AS dr2_id2 

FROM gaiadr3.dr2_neighbourhood AS cross3

INNER JOIN [username].godoyrivera AS mine ON mine.dr3_source_id=cross3.dr3_source_id

INNER JOIN gaiadr2.gaia_source AS gsource ON cross3.dr2_source_id=gsource.source_id

These files noted above are all called within the notebooks as if they were stored in the same folder as the notebook.

Also the files 'colmantess.csv' and 'spinspotter_by_star.csv' used to generate figures in figure10.ipynb are not publicly accessible. Please contact the relevant authors to get these files, and do note that the file names are not necessarily identical to what those authors provide (colmantess.csv was labeled c24_for_journal.csv when shared with us)