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Register a new userFliPer$_{Class}$: In search of solar-like pulsators among TESS targets
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The NASAs Transiting Exoplanet Survey Satellite (TESS) is about to provide full-frame images of almost the entire sky. The amount of stellar data to be analysed represents hundreds of millions stars, which is several orders of magnitude above the amount of stars observed by CoRoT, Kepler, or K2 missions. We aim at automatically classifying the newly observed stars, with near real-time algorithms, to better guide their subsequent detailed studies. In this paper, we present a classification algorithm built to recognise solar-like pulsators among classical pulsators, which relies on the global amount of power contained in the PSD, also known as the FliPer (Flicker in spectral Power density). As each type of pulsating star has a characteristic background or pulsation pattern, the shape of the PSD at different frequencies can be used to characterise the type of pulsating star. The FliPer Classifier (FliPer$_{Class}$) uses different FliPer parameters along with the effective temperature as input parameters to feed a machine learning algorithm in order to automatically classify the pulsating stars observed by TESS. Using noisy TESS simulated data from the TESS Asteroseismic Science Consortium (TASC), we manage to classify pulsators with a 98% accuracy. Among them, solar-like pulsating stars are recognised with a 99% accuracy, which is of great interest for further seismic analysis of these stars like our Sun. Similar results are obtained when training our classifier and applying it to 27 days subsets of real Kepler data. FliPer$_{Class}$ is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (TDA) classification working group.
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The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few thousand stars observed by the CoRoT, $textit{Kepler}$ and K2 missions. However, most of the TESS targets have not yet been properly classified and characterized. In order to improve the analysis of the TESS data, it is crucial to determine the type of stellar pulsations in a timely manner. We propose an automatic method to classify stars attending to their pulsation properties, in particular, to identify solar-like pulsators among all TESS targets. It relies on the use of the global amount of power contained in the power spectrum (already known as the FliPer method) as a key parameter, along with the effective temperature, to feed into a machine learning classifier. Our study, based on TESS simulated datasets, shows that we are able to classify pulsators with a $98%$ accuracy.
We study 23 previously published Kepler targets to perform a consistent grid-based Bayesian asteroseismic analysis and compare our results to those obtained via the Asteroseismic Modelling Portal (AMP). We find differences in the derived stellar parameters of many targets and their uncertainties. While some of these differences can be attributed to systematic effects between stellar evolutionary models, we show that the different methodologies deliver incompatible uncertainties for some parameters. Using non-adiabatic models and our capability to measure surface effects, we also investigate the dependency of these surface effects on the stellar parameters. Our results suggest a dependence of the magnitude of the surface effect on the mixing length parameter which also, but only minimally, affects the determination of stellar parameters. While some stars in our sample show no surface effect at all, the most significant surface effects are found for stars that are close to the Suns position in the HR diagram.
We use various method to extract surface rotation periods of Kepler targets exhibiting solar-like oscillations and compare their results.
Solar-like oscillations are excited in cool stars with convective envelopes and provide a powerful tool to constrain fundamental stellar properties and interior physics. We provide a brief history of the detection of solar-like oscillations, focusing in particular on the space-based photometry revolution started by the CoRoT and Kepler Missions. We then discuss some of the lessons learned from these missions, and highlight the continued importance of smaller space telescopes such as BRITE constellation to characterize very bright stars with independent observational constraints. As an example, we use BRITE observations to measure a tentative surface rotation period of 28.3+/-0.5 days for alpha Cen A, which has so far been poorly constrained. We also discuss the expected yields of solar-like oscillators from the TESS Mission, demonstrating that TESS will complement Kepler by discovering oscillations in a large number of nearby subgiants, and present first detections of oscillations in TESS exoplanet host stars.
The frequency of maximum oscillation power measured in dwarfs and giants exhibiting solar-like pulsations provides a precise, and potentially accurate, inference of the stellar surface gravity. An extensive comparison for about 40 well-studied pulsating stars with gravities derived using classical methods (ionisation balance, pressure-sensitive spectral features or location with respect to evolutionary tracks) supports the validity of this technique and reveals an overall remarkable agreement with mean differences not exceeding 0.05 dex (although with a dispersion of up to ~0.2 dex). It is argued that interpolation in theoretical isochrones may be the most precise way of estimating the gravity by traditional means in nearby dwarfs. Attention is drawn to the usefulness of seismic targets as benchmarks in the context of large-scale surveys.
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