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

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

Stellar activity contamination of radial velocity (RV) data is one of the top challenges plaguing the field of extreme-precision RV science. Previous work has shown that photometry can be very effective at removing such signals from RV data, especially stellar activity caused by rotating starspots a...

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Main Authors: Corey Beard, Paul Robertson, Jack Lubin, Te Han, Rae Holcomb, Pranav Premnath, R. Paul Butler, Paul A. Dalba, Brad Holden, Cullen H. Blake, Scott A. Diddams, Arvind F. Gupta, Samuel Halverson, Daniel M. Krolikowski, Dan Li, Andrea S.J. Lin, Sarah E. Logsdon, Emily Lubar, Suvrath Mahadevan, Michael W. McElwain, Joe P. Ninan, Leonardo A. Paredes, Arpita Roy, Christian Schwab, Gudmundur Stefansson, Ryan C. Terrien, Jason T. Wright
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:The Astronomical Journal
Subjects:
Exoplanets
Transits
Antarctic observatories
Radial velocity
Stellar activity
Online Access:https://doi.org/10.3847/1538-3881/ad9eb0
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Summary:Stellar activity contamination of radial velocity (RV) data is one of the top challenges plaguing the field of extreme-precision RV science. Previous work has shown that photometry can be very effective at removing such signals from RV data, especially stellar activity caused by rotating starspots 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 Transiting Exoplanet Survey Satellite (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 ( σ _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.
ISSN:1538-3881

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