Gaia astrometry disfavors a binary origin for long secondary periods: data products

This dataset contains the data products accompanying the paper “Gaia astrometry disfavors a binary origin for long secondary periods” by Cheyanne Shariat, Kareem El-Badry, Morgan MacLeod, and Emily Leiner (2026). The release includes the Gaia and ASAS-SN long-secondary-period (LSP) sample catalogs, the ellipsoidal-variable control samples, cached gaiamock prediction tables used for the RUWE forward modeling, the machine-readable Gaia BP/G/RP SED-fit table for the nearby sample (radii and Teff), and the full Gaia Focused Product Release time-series tables used in the RV and photometric analysis. Main files:epoch_RVs_LSPs.csv: full Gaia FPR multi-epoch radial-velocity table used for the LSP RV analysis.LSP_phot_timeseries.csv: full Gaia photometric time-series table for the LSP sample. gaiafpr_lpv_parent_topquality_dustcorr.fits: dust-corrected Gaia FPR LPV parent sample used for sample construction.tlsp_merged_gaia_edenhofer_corr.fits: merged Gaia LSP sample used in the main analysis.tab_merged_asasn_edenhofer_corr.fits: merged ASAS-SN LSP sample used for comparison tests.tab_ell.fits: ellipsoidal-variable control sample used in the RUWE comparison.asasn_ellip_gaia.fits: Gaia-matched ASAS-SN ellipsoidal-variable control sample.gaia_100pc_cleaned.parquet: cleaned local Gaia reference sample used for CMD comparisons. gaia_lsp_variant_predictions_1p5kpc.fits: cached Gaia DR3 RUWE forward-model predictions for the model-variation tests.asas_lsp_variant_predictions_1p5kpc.fits: cached ASAS-SN DR3 RUWE forward-model predictions for the model-variation tests.gaia_lsp_dr4_predictions_1p5kpc.fits: cached Gaia DR4-quality RUWE forward-model predictions.asas_lsp_dr4_predictions_1p5kpc.fits: cached ASAS-SN DR4-quality RUWE forward-model predictions. gaia_lsp_750pc_sed_fit_machine_readable.csv: machine-readable table of Gaia-only SED-fit temperatures and radii for the nearby sample. The code used to read these files and reproduce the paper figures is available at:https://github.com/cheyanneshariat/LSPs