Preparing for Advanced LIGO: A Star–Galaxy Separation Catalog for the Palomar Transient Factory
The search for fast optical transients, such as the expected electromagnetic counterparts to binary neutron star mergers, is riddled with false positives (FPs) ranging from asteroids to stellar flares. While moving objects are readily rejected via image pairs separated by ~1 hr, stellar flares represent a challenging foreground, significantly outnumbering rapidly evolving explosions. Identifying stellar sources close to and fainter than the transient detection limit can eliminate these FPs. Here, we present a method to reliably identify stars in deep co-adds of Palomar Transient Factory (PTF) imaging. Our machine-learning methodology utilizes the random forest (RF) algorithm, which is trained using > 3 x 10^6 sources with Sloan Digital Sky Survey (SDSS) spectra. When evaluated on an independent test set, the PTF RF model outperforms the SExtractor star classifier by ~4%. For faint sources (r' ≥ 21 mag), which dominate the field population, the PTF RF model produces a ~19% improvement over SExtractor. To avoid false negatives in the PTF transient-candidate stream, we adopt a conservative stellar classification threshold, corresponding to a galaxy misclassification rate of 0.005. Ultimately, 1.70 x 10^8 objects are included in our PTF point-source catalog, of which only ~10^6 are expected to be galaxies. We demonstrate that the PTF RF catalog reveals transients that otherwise would have been missed. To leverage its superior image quality, we additionally create an SDSS point-source catalog, which is also tuned to have a galaxy misclassification rate of 0.005. These catalogs have been incorporated into the PTF real-time pipelines to automatically reject stellar sources as non-extragalactic transients.
© 2017. The American Astronomical Society. Received 2016 August 30; revised 2016 November 16; accepted 2016 December 2; published 2017 January 16. This project started as part of an undergraduate research project at the California Institute of Technology. We thank T. Prince for funding M.K.K. during the summer of 2015. We are extremely grateful to A. Thakar and the entire SDSS CasJobs Helpdesk for assistance in performing the large cross-match between PTF and SDSS spectroscopic sources. Without their assistance this study would not have been possible. Without the patience and aid of R. Lupton we would not have been able to recreate the SDSS photometric classifier. We are in debt to M. M. Kasliwal, who endured countless conversations on the appropriate threshold for point-source classification. With gratitude, we salute S. B. Cenko for useful suggestions on the comparison of the NERSC catalog and the PTF RF catalog. Finally, we thank the anonymous referee for suggestions that improved this manuscript. A.A.M. acknowledges support for this work by NASA from Hubble Fellowship grant HST-HF-51325.01, awarded by STScI, operated by AURA, Inc., for NASA, under contract NAS 5-26555. Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy Office of Science. The SDSS-III Web site is http://www.sdss3.org/. Facilities: Sloan, PO:1.2m.
Published - Miller_2017_AJ_153_73.pdf