Finding, Characterizing, and Classifying Variable Sources in Multi-epoch Sky Surveys: QSOs and RR Lyrae in PS1 3π data

In area and depth, the Pan-STARRS1 (PS1) 3π survey is unique among many-epoch, multi-band surveys and has enormous potential for the all-sky identification of variable sources. PS1 has observed the sky typically seven times in each of its five bands (grizy) over 3.5 years, but unlike SDSS, not simultaneously across the bands. Here we develop a new approach for quantifying statistical properties of non-simultaneous, sparse, multi-color light curves through light curve structure functions, effectively turning PS1 into a ~35-epoch survey. We use this approach to estimate variability amplitudes and timescales (ωr, τ) for all point sources brighter than rP1 = 21.5 mag in the survey. With PS1 data on SDSS Stripe 82 as "ground truth," we use a Random Forest Classifier to identify QSOs and RR Lyrae based on their variability and their mean PS1 and WISE colors. We find that, aside from the Galactic plane, QSO and RR Lyrae samples of purity ~75% and completeness ~92% can be selected. On this basis we have identified a sample of ~1,000,000 QSO candidates, as well as an unprecedentedly large and deep sample of ~150,000 RR Lyrae candidates with distances from ~10 to ~120 kpc. Within the Draco dwarf spheroidal, we demonstrate a distance precision of 6% for RR Lyrae candidates. We provide a catalog of all likely variable point sources and likely QSOs in PS1, a total of 25.8 × 106 sources

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

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) $3\pi$ 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 PS1's sparse, asynchronous multi-band light curves ($\lesssim 12$ 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 $\gt 80 \%$ 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 Lyrae stars to date. The PS1 sample of ~45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period–luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey