Data from the TESS All-Sky Rotation Survey (TARS)

This Zenodo contains the large data files for the TESS All-Sky Rotation Survey (TARS; Boyle, Bouma, and Mann 2026).

The TARS paper was accepted on April 27, 2026. Version 2 of the TARS catalog contains an update to our period-doubling logic to account for the harmonic classifier's false positive rate. This update also boost the completeness of the TARS catalog by ~10%, increasing the number of stars in the TARS catalog from 944,056 to 1,046,317. The data files and code in this repository have been updated with the new catalog. Please download the new version of the catalog!

TARS is an all-sky catalog of stellar rotation period estimates for 1,046,317 stars with TESS magnitude T < 16 and distances within 500 pc, derived from TESS full-frame image light curves spanning Sectors 1–96 (2018 July – 2025 September). The catalog more than doubles the number of bright stars with known rotation periods within 100 pc and increases the count within 500 pc by a factor of 4.0. Two random forest classifiers provide per-measurement scores for separating instrumental systematics from astrophysical signals and for identifying half-period harmonics, enabling users to tune the trade-off between completeness and reliability for their science case.

Files

tars_table_2.feather — Our default catalog of 1,046,317 adopted rotation periods. This file contains one row per star, with the adopted rotation period, uncertainty, quality flags, Gaia astrometry and photometry, and sector-level classifier scores and calibrated Bayesian probabilities. The structure and column descriptions are given in Table 2 of the manuscript. This is the catalog most users will want to start with.

tars_table_2.csv.zip -- Same as tars_table_2.feather but in .CSV format.

tars_table_4.feather — The full catalog of all ~39 million sector-level rotation measurements for 7.5 million target stars. **Warning: this file is approximately 13 GB.** Each row represents one star–sector combination with Lomb-Scargle periodogram parameters, classifier probabilities, and Gaia cross-matched stellar properties. The column descriptions are given in Table 4 (Appendix C) of the manuscript. You will need this file if you want to regenerate the rotation catalog with different classifier thresholds or quality cuts using the master_validation.py script.

tars_table_4.csv.zip -- Same as tars_table_4.feather but in .CSV format. **Warning: This file is ~8 GB zipped and ~21 GB unzipped. It will take longer to read into python than the feather file.

validation_samples.zip — The four external validation samples used in Section 5 of the paper: Kepler/McQuillan (McQuillan et al. 2014), K2 (Reinhold & Hekker 2020), ZTF (Lu et al. 2022), and open clusters (Long et al. 2023). Each file contains the TESS rotation measurements for stars in the corresponding reference sample, with columns matching tars_table_4.feather plus a Prot column containing the literature rotation period. If you want to run the master_validation.py script described in the paper, download this file, uncompress it, and place the resulting validation_samples/ directory alongside the script.

master_validation.py -- A user can use this script to generate their own catalog of rotation periods with different choices than our default catalog. This will load the validation samples and apply the user's given input criteria to the validation samples to give an estimate of the completeness and reliability of the output period catalog built with the user's input parameters. Running this on my 2023 Mac Studio (M2 Ultra chip, 64 GB memory, 24 cores) took ~2 minutes to make the default TARS catalog.

master_validation_lowmem.py -- Same as master_validation.py, except optimized to run on computers that have less memory available. Running this on my 2023 Mac Studio (M2 Ultra chip, 64 GB memory, 24 cores) took ~1.5 minutes to make the default TARS catalog.

master_validation_requirements.txt - The python package versions that we used to run the master_validation.py script.

HOW_TO_USE.md -- Description of how to use the master_validation.py script.

boyle_tars_accepted.pdf -- The accepted version of the TARS paper (same as the arXiv submission).

Reproducing and Customizing the Catalog

We provide a Python script, master_validation.py, that allows users to regenerate the rotation catalog with different systematics classifier thresholds, alias classifier thresholds, quality flag selections, and alias-handling strategies. The script also runs the same validation tests from the paper (Section 5) so users can assess the completeness and reliability of their custom selections. Full usage instructions are available in HOW_TO_USE.md.

Light Curves and Vetting Plots

The 39 million TESS light curves and vetting plots used in this analysis are available as a MAST High-Level Science Product at https://archive.stsci.edu/hlsp/tars. This HLSP will become active upon paper acceptance.

Citation

If you use these data, please cite Boyle, Bouma, & Mann (2026).