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Joint Modeling of Radial Velocities and Photometry with a Gaussian Process Framework
Earth and Planetary Astrophysics · Submitted 1 May 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Using these synthetic datasets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.
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The abstract, sentence by sentence
Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. For most stars, the detection limit of planetary companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. Correlated signals caused by rotationally-modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of $\gtrsim$0.1 mag in brightness and $\gtrsim$100 m s$^{-1}$ in RV semi-amplitudes. Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. Our method, inspired by the $\textit{FF}^\prime$ framework of (Aigrain et al. 2012), models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. We also simulate time series affected by starspots by extending the $\texttt{starry}$ software (Luger et al. 2019) to incorporate time evolution of stellar features. Using these synthetic datasets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | For most stars, the detection limit of planetary companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. | background 1.00 | 0.07 | 0.02 | hedged possibility 0.87 |
| s2 | Correlated signals caused by rotationally-modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. | background 1.00 | 0.06 | 0.03 | asserted 0.56 |
| s3 | This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of $\gtrsim$0.1 mag in brightness and $\gtrsim$100 m s$^{-1}$ in RV semi-amplitudes. | background 1.00 | 0.08 | 0.02 | asserted 0.97 |
| s4 | Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. | background 0.93 | 0.04 | 0.04 | asserted 0.97 |
| s5 | Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. | background 0.99 | 0.05 | 0.03 | asserted 1.00 |
| s6 | However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. | gap 1.00 | 0.06 | 0.05 | asserted 1.00 |
| s7 | In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. | contribution 1.00 | 0.94 | 0.32 | asserted 0.95 |
| s8 | Our method, inspired by the $\textit{FF}^\prime$ framework of (Aigrain et al. 2012), models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. | method 0.99 | 0.79 | 0.09 | asserted 1.00 |
| s9 | We also simulate time series affected by starspots by extending the $\texttt{starry}$ software (Luger et al. 2019) to incorporate time evolution of stellar features. | method 0.93 | 0.93 | 0.03 | asserted 1.00 |
| s10 | Using these synthetic datasets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches. | result 1.00 | 0.96 | 0.53 | asserted 1.00 |
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The paper
Original abstract
Developments in the stability of modern spectrographs have led to extremely precise instrumental radial velocity (RV) measurements. For most stars, the detection limit of planetary companions with these instruments is expected to be dominated by astrophysical noise sources such as starspots. Correlated signals caused by rotationally-modulated starspots can obscure or mimic the Doppler shifts induced by even the closest, most massive planets. This is especially true for young, magnetically active stars where stellar activity can cause fluctuation amplitudes of $\gtrsim$0.1 mag in brightness and $\gtrsim$100 m s$^{-1}$ in RV semi-amplitudes. Techniques that can mitigate these effects and increase our sensitivity to young planets are critical to improving our understanding of the evolution of planetary systems. Gaussian processes (GPs) have been successfully employed to model and constrain activity signals in individual cases. However, a principled approach of this technique, specifically for the joint modeling of photometry and RVs, has not yet been developed. In this work, we present a GP framework to simultaneously model stellar activity signals in photometry and RVs that can be used to investigate the relationship between both time series. Our method, inspired by the $\textit{FF}^\prime$ framework of (Aigrain et al. 2012), models spot-driven activity signals as the linear combinations of two independent latent GPs and their time derivatives. We also simulate time series affected by starspots by extending the $\texttt{starry}$ software (Luger et al. 2019) to incorporate time evolution of stellar features. Using these synthetic datasets, we show that our method can predict spot-driven RV variations with greater accuracy than other GP approaches.
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How the literature uses it
3 arXiv papers cite this in 3 sentences
Papers citing it · 2 sentences without a confident label
Cites it as background 1 paper
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There have been recent developments in using advanced flexible models like Gaussian Process (GP) regression to model quasi-periodic stellar activity signals and reveal planet signals [ref] .
Characterization of K2-167 b and CALM, a new stellar activity mitigation method · Introduction · 0.97
What it cites · 7 sentences without a confident label
Cites as background 1 paper
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These techniques range in complexity, beginning with using a GP as a correlated red noise filter [ref] to leveraging information from ancillary photometery to further constrain the GP trained on the RVs [ref] , to jointly modeling spectrosphic activity indicators and RVs with a multidimensional GP in order to constrain the activity signals [ref] .
Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity · Introduction · 0.94
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