Jitter Across 15 Years: Leveraging Precise Photometry from Kepler and TESS to Extract Exoplanets from Radial Velocity Time Series

TL;DR

This study evaluates how precise photometry from Kepler and TESS can help mitigate stellar activity signals in radial velocity data, improving exoplanet detection accuracy.

Contribution

It compares the effectiveness of Kepler and TESS photometry in removing stellar activity noise from RV data using injection recovery tests and models trained on simultaneous observations.

Findings

Kepler photometry generally outperforms TESS for noise removal due to longer baseline and higher precision.

TESS can be more effective for active stars with high-precision RVs, especially when Kepler data is unavailable.

Training on simultaneous photometry does not always yield the best noise removal results.

Abstract

Stellar activity contamination of radial velocity (RV) data is one of the top challenges plaguing the field of extreme precision RV (EPRV) science. Previous work has shown that photometry can be very effective at removing such signals from RV data, especially stellar activity caused by rotating star spots and plage.The exact utility of photometry for removing RV activity contamination, and the best way to apply it, is not well known. We present a combination photometric and RV study of eight Kepler/K2 FGK stars with known stellar variability. We use NEID RVs acquired simultaneously with TESS photometry, and we perform injection recovery tests to quantify the efficacy of recent TESS photometry versus archival Kepler/K2 photometry for removing stellar variability from RVs. We additionally experiment with different TESS sectors when training our models in order to quantify the real benefit of simultaneously acquired RVs and photometry. We conclude that Kepler photometry typically performs better than TESS at removing noise from RV data when it is available, likely due to longer baseline and precision. In contrast, for targets with available K2 photometry, especially those most active, and with high precision ($\sigma_{NEID}$ $<$ 1 m s$^{-1}$) NEID RVs, TESS may be the more informative dataset. However, contrary to expectations, we have found that training on simultaneous photometry does not always achieve the best results.