Refining the Stellar Parameters of τ Ceti: a Pole-on Solar Analog

Korolik, Maria; Roettenbacher, Rachael M.; Fischer, Debra A.; Kane, Stephen R.; Perkins, Jean M.; Monnier, John D.; Davies, Claire L.; Kraus, Stefan; Le Bouquin, Jean-Baptiste; Anugu, Narsireddy; Gardner, Tyler; Lanthermann, Cyprien; Schaefer, Gail H.; Setterholm, Benjamin; Brewer, John M.; Llama, Joe; Zhao, Lily L.; Szymkowiak, Andrew E.; Henry, Gregory W.

doi:10.3847/1538-3881/ace906

Cited by 7 publications

(1 citation statement)

References 75 publications

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“…In such a scenario the polar spot could manifest as a large central spot. Examples such as the HAT-P-11 system, which consists of an active K4-dwarf hosting two highly misaligned planets (e.g., Sanchis-Ojeda & Winn 2011;Morris et al 2017;Yee et al 2018) and the almost pole-on, solar-type star τ Ceti, which is frequently included in target lists for exoplanet searches (Korolik et al 2023) both indicate that observing a system with such geometry in the future cannot be ruled out. Note.…”

Section: Preliminary Investigations Into Multiple-spot Casesmentioning

confidence: 99%

Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimized Retrieval Framework

Thompson,

Biagini,

Cracchiolo

et al. 2024

ApJ

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The chromatic contamination that arises from photospheric heterogeneities, e.g., spots and faculae on the host star presents a significant noise source for exoplanet transmission spectra. If this contamination is not corrected for, it can introduce substantial bias in our analysis of the planetary atmosphere. We utilize two stellar models of differing complexity, StARPA (Stellar Activity Removal for Planetary Atmospheres) and ASteRA (Active Stellar Retrieval Algorithm), to explore the biases introduced by stellar contamination in retrieval under differing degrees of stellar activity. We use the retrieval framework TauREx3 and a grid of 27 synthetic, spot-contaminated transmission spectra to investigate potential biases and to determine how complex our stellar models must be in order to accurately extract the planetary parameters from transmission spectra. The input observation is generated using the more complex model (StARPA), in which the spot latitude is an additional, fixable parameter. This observation is then fed into a combined stellar-planetary retrieval, which contains a simplified stellar model (ASteRA). Our results confirm that the inclusion of stellar activity parameters in retrieval minimizes bias under all activity regimes considered. ASteRA performs very well under low-to-moderate activity conditions, retrieving the planetary parameters with a high degree of accuracy. For the most active cases, characterized by larger, higher-temperature contrast spots, some minor residual bias remains due to ASteRA neglecting the interplay between the spot and the limb-darkening effect. As a result of this, we find larger errors in retrieved planetary parameters for central spots (0°) and those found close to the limb (60°) than those at intermediate latitudes (30°).

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Section: Preliminary Investigations Into Multiple-spot Casesmentioning

confidence: 99%

Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimized Retrieval Framework

Thompson,

Biagini,

Cracchiolo

et al. 2024

ApJ

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The River of Stars

Walsh

2024

Science and Fiction

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Improving Earth-like planet detection in radial velocity using deep learning

Zhao,

Dumusque,

Cretignier

et al. 2024

A&A

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Many novel methods have been proposed to mitigate stellar activity for exoplanet detection as the presence of stellar activity in radial velocity (RV) measurements is the current major limitation. Unlike traditional methods that model stellar activity in the RV domain, more methods are moving in the direction of disentangling stellar activity at the spectral level. As deep neural networks have already been proven to be one of the most effective tools in data mining, in this work, we explore their potential in the context of Earth-like planet detection in RV measurements. The goal of this paper is to present a novel convolutional neural network-based algorithm that efficiently models stellar activity signals at the spectral level, enhancing the detection of Earth-like planets. Based on the idea that the presence of planets can only produce a Doppler shift at the spectral level while the presence of stellar activity can introduce a variation in the profile of spectral lines (asymmetry and depth change), we trained a convolutional neural network to build the correlation between the change in the spectral line profile and the corresponding RV, full width at half maximum (FWHM) and bisector span (BIS) values derived from the classical cross-correlation function. This algorithm has been tested on three intensively observed stars: Alpha Centauri B (HD\,128621), Tau ceti (HD\,10700), and the Sun. By injecting simulated planetary signals at the spectral level, we demonstrate that our machine learning algorithm can achieve, for HD\,128621 and HD\,10700, a detection threshold of 0.5 m/s in semi-amplitude for planets with periods ranging from 10 to 300 days. This threshold would correspond to the detection of a sim 4\,$ M oplus $ in the habitable zone of those stars. On the HARPS-N solar dataset, thanks to significantly more data, our algorithm is even more efficient at mitigating stellar activity signals and can reach a threshold of 0.2 m/s, which would correspond to a 2.2\,$ M oplus $ planet on the orbit of the Earth. To the best of our knowledge, it is the first time that such low detection thresholds are reported for the Sun, but also for other stars, and therefore this highlights the efficiency of our convolutional neural network-based algorithm at mitigating stellar activity in RV measurements.

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Refining the Stellar Parameters of τ Ceti: a Pole-on Solar Analog

Cited by 7 publications

References 75 publications

Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimized Retrieval Framework

Correcting Exoplanet Transmission Spectra for Stellar Activity with an Optimized Retrieval Framework

The River of Stars

Improving Earth-like planet detection in radial velocity using deep learning

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