@article{11613, abstract = {Over 2,000 stars were observed for 1 month with a high enough cadence in order to look for acoustic modes during the survey phase of the Kepler mission. Solar-like oscillations have been detected in about 540 stars. The question of why no oscillations were detected in the remaining stars is still open. Previous works explained the non-detection of modes with the high level of magnetic activity of the stars. However, the sample of stars studied contained some classical pulsators and red giants that could have biased the results. In this work, we revisit this analysis on a cleaner sample of main-sequence solar-like stars that consists of 1,014 stars. First we compute the predicted amplitude of the modes of that sample and for the stars with detected oscillation and compare it to the noise at high frequency in the power spectrum. We find that the stars with detected modes have an amplitude to noise ratio larger than 0.94. We measure reliable rotation periods and the associated photometric magnetic index for 684 stars out of the full sample and in particular for 323 stars where the amplitude of the modes is predicted to be high enough to be detected. We find that among these 323 stars 32% of them have a level of magnetic activity larger than the Sun during its maximum activity, explaining the non-detection of acoustic modes. Interestingly, magnetic activity cannot be the primary reason responsible for the absence of detectable modes in the remaining 68% of the stars without acoustic modes detected and with reliable rotation periods. Thus, we investigate metallicity, inclination angle of the rotation axis, and binarity as possible causes of low mode amplitudes. Using spectroscopic observations for a subsample, we find that a low metallicity could be the reason for suppressed modes. No clear correlation with binarity nor inclination is found. We also derive the lower limit for our photometric activity index (of 20–30 ppm) below which rotation and magnetic activity are not detected. Finally, with our analysis we conclude that stars with a photometric activity index larger than 2,000 ppm have 98.3% probability of not having oscillations detected.}, author = {Mathur, Savita and García, Rafael A. and Bugnet, Lisa Annabelle and Santos, Ângela R.G. and Santiago, Netsha and Beck, Paul G.}, issn = {2296-987X}, journal = {Frontiers in Astronomy and Space Sciences}, keywords = {Astronomy and Astrophysics}, publisher = {Frontiers Media}, title = {{Revisiting the impact of stellar magnetic activity on the detectability of solar-like oscillations by Kepler}}, doi = {10.3389/fspas.2019.00046}, volume = {6}, year = {2019}, } @article{11615, abstract = {The recently published Kepler mission Data Release 25 (DR25) reported on ∼197 000 targets observed during the mission. Despite this, no wide search for red giants showing solar-like oscillations have been made across all stars observed in Kepler’s long-cadence mode. In this work, we perform this task using custom apertures on the Kepler pixel files and detect oscillations in 21 914 stars, representing the largest sample of solar-like oscillating stars to date. We measure their frequency at maximum power, νmax, down to νmax≃4μHz and obtain log (g) estimates with a typical uncertainty below 0.05 dex, which is superior to typical measurements from spectroscopy. Additionally, the νmax distribution of our detections show good agreement with results from a simulated model of the Milky Way, with a ratio of observed to predicted stars of 0.992 for stars with 10<νmax<270μHz. Among our red giant detections, we find 909 to be dwarf/subgiant stars whose flux signal is polluted by a neighbouring giant as a result of using larger photometric apertures than those used by the NASA Kepler science processing pipeline. We further find that only 293 of the polluting giants are known Kepler targets. The remainder comprises over 600 newly identified oscillating red giants, with many expected to belong to the Galactic halo, serendipitously falling within the Kepler pixel files of targeted stars.}, author = {Hon, Marc and Stello, Dennis and García, Rafael A and Mathur, Savita and Sharma, Sanjib and Colman, Isabel L and Bugnet, Lisa Annabelle}, issn = {1365-2966}, journal = {Monthly Notices of the Royal Astronomical Society}, keywords = {Space and Planetary Science, Astronomy and Astrophysics, asteroseismology, methods: data analysis, techniques: image processing, stars: oscillations, stars: statistics}, number = {4}, pages = {5616--5630}, publisher = {Oxford University Press}, title = {{A search for red giant solar-like oscillations in all Kepler data}}, doi = {10.1093/mnras/stz622}, volume = {485}, year = {2019}, } @article{11614, 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 (FliPerClass) 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. FliPerClass is part of the large TASC classification pipeline developed by the TESS Data for Asteroseismology (T’DA) classification working group.}, author = {Bugnet, Lisa Annabelle and García, R. A. and Mathur, S. and Davies, G. R. and Hall, O. J. and Lund, M. N. and Rendle, B. M.}, issn = {1432-0746}, journal = {Astronomy & Astrophysics}, keywords = {Space and Planetary Science, Astronomy and Astrophysics}, publisher = {EDP Science}, title = {{FliPerClass: In search of solar-like pulsators among TESS targets}}, doi = {10.1051/0004-6361/201834780}, volume = {624}, year = {2019}, } @article{11623, abstract = {Brightness variations due to dark spots on the stellar surface encode information about stellar surface rotation and magnetic activity. In this work, we analyze the Kepler long-cadence data of 26,521 main-sequence stars of spectral types M and K in order to measure their surface rotation and photometric activity level. Rotation-period estimates are obtained by the combination of a wavelet analysis and autocorrelation function of the light curves. Reliable rotation estimates are determined by comparing the results from the different rotation diagnostics and four data sets. We also measure the photometric activity proxy Sph using the amplitude of the flux variations on an appropriate timescale. We report rotation periods and photometric activity proxies for about 60% of the sample, including 4431 targets for which McQuillan et al. did not report a rotation period. For the common targets with rotation estimates in this study and in McQuillan et al., our rotation periods agree within 99%. In this work, we also identify potential polluters, such as misclassified red giants and classical pulsator candidates. Within the parameter range we study, there is a mild tendency for hotter stars to have shorter rotation periods. The photometric activity proxy spans a wider range of values with increasing effective temperature. The rotation period and photometric activity proxy are also related, with Sph being larger for fast rotators. Similar to McQuillan et al., we find a bimodal distribution of rotation periods.}, author = {Santos, A. R. G. and García, R. A. and Mathur, S. and Bugnet, Lisa Annabelle and van Saders, J. L. and Metcalfe, T. S. and Simonian, G. V. A. and Pinsonneault, M. H.}, issn = {0067-0049}, journal = {The Astrophysical Journal Supplement Series}, keywords = {Space and Planetary Science, Astronomy and Astrophysics, methods: data analysis, stars: activity, stars: low-mass, stars: rotation, starspots, techniques: photometric}, number = {1}, publisher = {IOP Publishing}, title = {{Surface rotation and photometric activity for Kepler targets. I. M and K main-sequence stars}}, doi = {10.3847/1538-4365/ab3b56}, volume = {244}, year = {2019}, } @unpublished{11627, abstract = {For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA mission Kepler. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data analysis treatment.}, author = {Breton, S. N. and Bugnet, Lisa Annabelle and Santos, A. R. G. and Saux, A. Le and Mathur, S. and Palle, P. L. and Garcia, R. A.}, booktitle = {arXiv}, keywords = {asteroseismology, rotation, solar-like stars, kepler, machine learning, random forest}, title = {{Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques}}, doi = {10.48550/arXiv.1906.09609}, year = {2019}, }