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  • 标题:Leveraging Space-based Data from the Nearest Solar-type Star to Better Understand Stellar Activity Signatures in Radial Velocity Data
  • 本地全文:下载
  • 作者:Tamar Ervin ; Samuel Halverson ; Abigail Burrows
  • 期刊名称:The Astronomical journal
  • 印刷版ISSN:0004-6256
  • 电子版ISSN:1538-3881
  • 出版年度:2022
  • 卷号:163
  • 期号:6
  • 页码:1-16
  • DOI:10.3847/1538-3881/ac67e6
  • 语种:English
  • 出版社:American Institute of Physics
  • 摘要:Stellar variability is a key obstacle in reaching the sensitivity required to recover Earth-like exoplanetary signals using the radial velocity (RV) detection method. To explore activity signatures in Sun-like stars, we present SolAster, a publicly distributed analysis pipeline10that allows for comparison of space-based measurements with ground-based disk-integrated RVs. Using high-spatial-resolution Dopplergrams, magnetograms, and continuum filtergrams from the Helioseismic and Magnetic Imager aboard the Solar Dynamics Observatory (SDO), we estimate "Sun-as-a-star" disk-integrated RVs due to rotationally modulated flux imbalances and convective blueshift suppression, as well as other observables such as unsigned magnetic flux. Comparing these measurements with ground-based RVs from the NEID instrument, which observes the Sun daily using an automated solar telescope, we find a strong relationship between magnetic activity indicators and RV variation, supporting efforts to examine unsigned magnetic flux as a proxy for stellar activity in slowly rotating stars. Detrending against measured unsigned magnetic flux allows us to improve the NEID RV measurements by ∼20% (∼50 cm s−1 in a quadrature sum), yielding an rms scatter of ∼60 cm s−1 over five months. We also explore correlations between individual and averaged spectral line shapes in the NEID spectra and SDO-derived magnetic activity indicators, motivating future studies of these observables. Finally, applying SolAster to archival planetary transits of Venus and Mercury, we demonstrate the ability to recover small amplitude (<50 cm s−1) RV variations in the SDO data by directly measuring the Rossiter–McLaughlin signals.
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