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RV×TESS. I. Modeling Asteroseismic Signals with Simultaneous Photometry and Radial Velocities

  • Jiaxin Tang
  • , Sharon X. Wang
  • , Yaguang Li
  • , Timothy R. Bedding
  • , Guang Yao Xiao
  • , Fabo Feng
  • , Jie Yu
  • , Zun Wang
  • , Jennifer A. Burt
  • , R. Paul Butler
  • , Brad Carter
  • , Jeffrey D. Crane
  • , Matías R. Díaz
  • , Samuel K. Grunblatt
  • , Daniel Huber
  • , Hugh Jones
  • , Stephen R. Kane
  • , Jacob K. Luhn
  • , Stephen A. Shectman
  • , Johanna Teske
  • Rob Wittenmyer, Jason T. Wright, Jeremy Bailey, Simon J. O’Toole, Chris G. Tinney
  • Tsinghua University
  • University of Hawai'i at Mānoa
  • The University of Sydney
  • Shanghai Jiao Tong University
  • Australian National University
  • King's College London
  • California Institute of Technology
  • Carnegie Institution of Washington
  • University of Southern Queensland
  • University of Alabama
  • University of Hertfordshire
  • University of California at Riverside
  • Pennsylvania State University
  • University of New South Wales
  • Macquarie University

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Detecting small planets via the radial velocity (RV) method remains challenged by signals induced by stellar variability, versus the effects of the planet(s). Here, we explore using Gaussian process (GP) regression with Transiting Exoplanet Survey Satellite (TESS) photometry in modeling RVs to help to mitigate stellar jitter from oscillations and granulation for exoplanet detection. We applied GP regression to simultaneous TESS photometric and RV data of HD 5562, a G-type subgiant (M = 1.09M, R = 1.88R) with a V magnitude of 7.17, using photometry to inform the priors for RV fitting. The RV data is obtained by the Magellan Planet Finder Spectrograph. The photometry-informed GP regression reduced the RV scatter of HD 5562 from 2.03 to 0.51 m s−1. We performed injection and recovery tests to evaluate the potential of GPs for discovering small exoplanets around evolved stars, which demonstrate that the GP provides comparable noise reduction to the binning method. We also found that the necessity of photometric data depends on the quality of the RV dataset. For long baseline and high-cadence RV observations, GP regression can effectively mitigate stellar jitter without photometric data. However, for intermittent RV observations, incorporating photometric data improves GP fitting and enhances detection capabilities.

Original languageEnglish
Article number123
JournalAstronomical Journal
Volume171
Issue number3
DOIs
Publication statusPublished - 2 Mar 2026
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2026. The Author(s). Published by the American Astronomical Society.

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