AAS Nova

Selections from 2017: Computers Help Us Map Our Home

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Milky Way
The structure of the Milky Way is challenging to understand from our position inside of it. One way of approaching this problem is to build a catalog of stars that trace the shape of the galaxy's halo. [ESO / S. Brunier]

Editor’s note: In these last two weeks of 2017, we’ll be looking at a few selections that we haven’t yet discussed on AAS Nova from among the most-downloaded papers published in AAS journals this year. The usual posting schedule will resume in January.

Machine-Learned Identification of RR Lyrae Stars from Sparse, Multi-Band Data: The PS1 Sample

Published April 2017

 

Main takeaway:

A sample of RR Lyrae variable stars was built from the Pan-STARRS1 (PS1) survey by a team led by Branimir Sesar (Max Planck Institute for Astronomy, Germany). The sample of 45,000 stars represents the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date.

Why it’s interesting:

It’s challenging to understand the overall shape and behavior of our galaxy because we’re stuck on the inside of it. RR Lyrae stars are a useful tool for this purpose: they can be used as tracers to map out the Milky Way’s halo. The authors’ large sample of RR Lyrae stars from PS1 — combined with proper-motion measurements from Gaia and radial-velocity measurements from multi-object spectroscopic surveys — could become the premier source for studying the structure, kinematics, and the gravitational potential of our galaxy’s outskirts.

How they were found:

RR Lyrae sample

The black dots show the distribution of the 45,000 probable RR Lyrae stars in the authors’ sample. [Sesar et al. 2017]

The 45,000 stars in this sample were selected not by humans, but by computer. The authors used machine-learning algorithms to examine the light curves in the Pan-STARRS1 sample and identify the characteristic brightness variations of RR Lyrae stars lying in the galactic halo. These techniques resulted in a very pure and complete sample, and the authors suggest that this approach may translate well to other sparse, multi-band data sets — such as that from the upcoming Large Synoptic Survey Telescope (LSST) galactic plane sub-survey.

Citation

Branimir Sesar et al 2017 AJ 153 204. doi:10.3847/1538-3881/aa661b