The Astronomical Journal Volume 153(5)

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Year: 2017, Volume: 153(5)

Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample Creative Commons

Branimir Sesar,

Nina Hernitschek,

Sandra Mitrović,

Željko Ivezić,

Hans-Walter Rix,

Judith G. Cohen,

Edouard J. Bernard,

Eva K. Grebel,

Nicolas F. Martin,

Edward F. Schlafly,

William S. Burgett,

Peter W. Draper,

Heather Flewelling,

Nick Kaiser,

Rolf P. Kudritzki,

Eugene A. Magnier,

Nigel Metcalfe,

John L. Tonry,

Christopher Waters

The Astronomical Journal, Journal Year: 2017, Volume and Issue: 153(5), P. 1 - 1

Published: May 1, 2017

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRSl (PSI) Зтг survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PSl’s sparse, asynchronous multi-band light curves (<12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in >80% of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machine- learned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR …