FliPer_Class: In search of solar-like pulsators among TESS targets

¹ IRFU, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
e-mail: lisa.bugnet@cea.fr
² AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, 91191 Gif-sur-Yvette, France
³ Instituto de Astrofísica de Canarias, 38200 La Laguna, Tenerife, Spain
⁴ Universidad de La Laguna, Dpto. de Astrofísica, 38205 La Laguna, Tenerife, Spain
⁵ School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
⁶ Stellar Astrophysics Centre, Department of Physics and Astronomy, Aarhus University, Ny Munkegade 120, 8000 Aarhus C, Denmark

Received: 5 December 2018
Accepted: 25 February 2019

Abstract

The NASA 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 more than the number of stars observed by the Convection, Rotation and planetary Transits satellite (CoRoT), and NASA Kepler and K2 missions. We aim at automatically classifying the newly observed stars with near real-time algorithms to better guide the subsequent detailed studies. In this paper, we present a classification algorithm built to recognise solar-like pulsators among classical pulsators. This algorithm relies on the global amount of power contained in the power spectral density (PSD), also known as the flicker in spectral power density (FliPer). Because 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 ML 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 classify pulsators with a 98% accuracy. Among them, solar-like pulsating stars are recognised with a 99% accuracy, which is of great interest for a further seismic analysis of these stars, which are like our Sun. Similar results are obtained when we trained our classifier and applied it to 27-day subsets of real Kepler data. FliPer_Class is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (T’DA) classification working group.