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Mikkel Nørup Lund

Inference of Stellar Parameters from Brightness Variations

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  • Melissa K. Ness, Flatiron Inst, Ctr Computat Astrophys
  • ,
  • Victor Silva Aguirre
  • Mikkel N. Lund
  • Matteo Cantiello, Flatiron Inst, Ctr Computat Astrophys
  • ,
  • Daniel Foreman-Mackey, Flatiron Inst, Ctr Computat Astrophys
  • ,
  • David W. Hogg, NYU, New York University, Dept Phys, Ctr Cosmol & Particle Phys
  • ,
  • Ruth Angus, Columbia Univ, Columbia University, Dept Astron, Pupin Phys Labs

It has been demonstrated that the time variability of a star's brightness at different frequencies can be used to infer its surface gravity, radius, mass, and age. With large samples of light curves now available from Kepler and K2, and upcoming surveys like TESS, we wish to quantify the overall information content of this data and identify where the information resides. As a first look into this question, we ask which stellar parameters we can predict from the brightness variations in red-giant stars data and to what precision, using a data-driven, nonparametric model. We demonstrate that the long-cadence (30 minute) Kepler light curves for 2000 red-giant stars can be used to predict their T(eff )and log g. Our inference makes use of a data-driven model of a part of the autocorrelation function (ACF) of the light curve, where we posit a polynomial relationship between stellar parameters and the ACF pixel values. We find that this model, trained using 1000 stars, can be used to recover the temperature T(eff )to

OriginalsprogEngelsk
Artikelnummer15
TidsskriftAstrophysical Journal
Vol/bind866
Nummer1
Antal sider9
ISSN0004-637X
DOI
StatusUdgivet - 10 okt. 2018

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