Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity

Jones, David; Stenning, David C.; Wolpert, Robert L.; Loredo, Thomas J.; Gilbertson, Christian; Dumusque, X.

doi:10.48550/arxiv.1711.01318

Cited by 45 publications

(61 citation statements)

References 19 publications

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“…Because the SAFE is not expected to be correlated with the apparent RV, it is primarily designed to detect, rather than directly correct for, the presence of stellar activity. However, the SAFE or the individual fitted coefficient values of Equation ( 9) may still be used to help correct the apparent RV by including it in a time-series model such as that of Rajpaul et al (2015) or Jones et al (2017). While the approach for cleansing the RV measurements of stellar activity by simply removing observations with a large value of SAFE may be useful for stars with intermittent activity, it would result in rejecting a large fraction of data for other stars that frequently have detectable levels of stellar activity.…”

Section: Discussionmentioning

confidence: 99%

“…In the exoplanet discovery community focused on extreme precision radial velocity (RV) measurements, better understanding the activity that takes place in the atmospheres of stars is an important goal (Fischer et al 2016;Dumusque et al 2017;Davis et al 2017;Jones et al 2017;Dumusque 2018;Cretignier et al 2020). One reason why this is the case is because stellar activity can produce RV's that mimic, or hide, the signal induced by an orbiting exoplanet (Saar & Donahue 1997;Queloz et al 2001;Desort et al 2007;Meunier et al 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Other developed statistics, not calculated from the CCF, instead look for a change in specific absorption features that are physically known to be sensitive to the stellar magnetic field (e.g., Queloz et al 2009;Pont et al 2011), such as emission in the core of the Hα line (Giguere et al 2016). More recent studies develop indicators that are primarily data-driven (Davis et al 2017;Jones et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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A Stellar Activity F-statistic for Exoplanet Surveys (SAFE)

Holzer,

Cisewski-Kehe,

Zhao

et al. 2021

Preprint

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In the search for planets orbiting distant stars the presence of stellar activity in the atmospheres of observed stars can obscure the radial velocity signal used to detect such planets. Furthermore, this stellar activity contamination is set by the star itself and cannot simply be avoided with better instrumentation. Various stellar activity indicators have been developed that may correlate with this contamination. We introduce a new stellar activity indicator called the Stellar Activity F-statistic for Exoplanet surveys (SAFE) that has higher statistical power (i.e., probability of detecting a true stellar activity signal) than many traditional stellar activity indicators in a simulation study of an active region on a Sun-like star with moderate to high signal-to-noise. Also through simulation, the SAFE is demonstrated to be associated with the projected area on the visible side of the star covered by active regions. We also demonstrate that the SAFE detects statistically significant stellar activity in most of the spectra for HD 22049, a star known to have high stellar variability. Additionally, the SAFE is calculated for recent observations of the three low-variability stars HD 34411, HD 10700, and HD 3651, the latter of which is known to have a planetary companion. As expected, the SAFE for these three only occasionally detects activity. Furthermore, initial exploration appears to indicate that the SAFE may be useful for disentangling stellar activity signals from planet-induced Doppler shifts.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Stellar Activity F-statistic for Exoplanet Surveys (SAFE)

Holzer,

Cisewski-Kehe,

Zhao

et al. 2021

Preprint

Self Cite

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“…On the other hand, the model also can not incorporate any prior knowledge of stellar or telluric contamination and does not distinguish between different forms of contamination whether stellar, terrestrial, or instrumental. The Doppler-Constrained Principal Components Analysis method, submitted by the PennState team, identifies the largest variations in RV shifted spectral data using PCA (Jones et al 2017). The resultant principal components highlight where the spectra is changing the most while the corresponding amplitudes of each principal component captures the magnitude of this change for each observation.…”

Section: C4 Pwgpmentioning

confidence: 99%

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Zhao,

Fischer,

Ford

et al. 2022

Preprint

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Measured spectral shifts due to intrinsic stellar variability (e.g., pulsations, granulation) and activity (e.g., spots, plages) are the largest source of error for extreme precision radial velocity (EPRV) exoplanet detection. Several methods are designed to disentangle stellar signals from true center-of-mass shifts due to planets. The EXPRES Stellar Signals Project (ESSP) presents a self-consistent comparison of 22 different methods tested on the same extreme-precision spectroscopic data from EXPRES. Methods derived new activity indicators, constructed models for mapping an indicator to the needed RV correction, or separated out shape-and shift-driven RV components. Since no ground truth is known when using real data, relative method performance is assessed using the total and nightly scatter of returned RVs and agreement between the results of different methods. Nearly all submitted methods return a lower RV RMS than classic linear decorrelation, but no method is yet consistently reducing the RV RMS to sub-meter-per-second levels. There is a concerning lack of agreement between the RVs returned by different methods. These results suggest that continued progress in this field necessitates increased interpretability of methods, high-cadence data to capture stellar signals at all timescales, and continued tests like the ESSP using consistent data sets with more advanced metrics for method performance. Future comparisons should make use of various well-characterized data sets-such as solar data or data with known injected planetary and/or stellar signals-to better understand method performance and whether planetary signals are preserved.

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“…This reduces the risk of the GP overfitting, that is, the absorbtion of planetary signals, since those signals are only present in the RV time series. This framework can be straightforwardly generalized to account for additional indicators, for the combination of several GP with different amplitudes, or even for the second order derivatives of the GP (e.g., Jones et al 2017). While this framework is very powerful in modeling stellar activity, it represents a challenge in terms of computational cost.…”

Section: Introductionmentioning

confidence: 99%

Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes

Delisle,

Unger,

Hara

et al. 2022

Preprint

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The radial velocity method is a very productive technique used to detect and confirm extrasolar planets. The most recent spectrographs, such as ESPRESSO or EXPRES, have the potential to detect Earth-like planets around Sun-like stars. However, stellar activity can induce radial velocity variations that dilute or even mimic the signature of a planet. A widely recognized method for disentangling these signals is to model the radial velocity time series, jointly with stellar activity indicators, using Gaussian processes and their derivatives. However, such modeling is prohibitive in terms of computational resources for large data sets, as the cost typically scales as the total number of measurements cubed. Here, we present s+leaf 2, a Gaussian process framework that can be used to jointly model several time series, with a computational cost that scales linearly with the data set size. This framework thus provides a state-of-the-art Gaussian process model, with tractable computations even for large data sets. We illustrate the power of this framework by reanalyzing the 246 HARPS radial velocity measurements of the nearby K2 dwarf HD 138038, together with two activity indicators. We reproduce the results of a previous analysis of these data, but with a strongly decreased computational cost (more than two order of magnitude). The gain would be even greater for larger data sets.

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Improving Exoplanet Detection Power: Multivariate Gaussian Process Models for Stellar Activity

Cited by 45 publications

References 19 publications

A Stellar Activity F-statistic for Exoplanet Surveys (SAFE)

A Stellar Activity F-statistic for Exoplanet Surveys (SAFE)

The EXPRES Stellar Signals Project II. State of the Field in Disentangling Photospheric Velocities

Efficient modeling of correlated noise. III. Scalable methods for jointly modeling several observables' time series with Gaussian processes

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