Automated Transient Identification in the Dark Energy Survey
Abstract
We describe an algorithm for identifying point-source transients and moving objects on reference-subtracted optical images containing artifacts of processing and instrumentation. The algorithm makes use of the supervised machine learning technique known as Random Forest. We present results from its use in the Dark Energy Survey Supernova program (DES-SN), where it was trained using a sample of 898,963 signal and background events generated by the transient detection pipeline. After reprocessing the data collected during the first DES-SN observing season (Sep. 2013 through Feb. 2014) using the algorithm, the number of transient candidates eligible for human scanning decreased by a factor of 13.4, while only 1 percent of the artificial Type Ia supernovae (SNe) injected into search images to monitor survey efficiency were lost, most of which were very faint events. Here we characterize the algorithm's performance in detail, and we discuss how it can inform pipeline design decisions for future time-domain imaging surveys, such as the Large Synoptic Survey Telescope and the Zwicky Transient Facility. An implementation of the algorithm and the training data used in this paper are available at http://portal.nersc.gov/project/dessn/autoscan.
Cite
@article{arxiv.1504.02936,
title = {Automated Transient Identification in the Dark Energy Survey},
author = {D. A. Goldstein and C. B. D'Andrea and J. A. Fischer and R. J. Foley and R. R. Gupta and R. Kessler and A. G. Kim and R. C. Nichol and P. Nugent and A. Papadopoulos and M. Sako and M. Smith and M. Sullivan and R. C. Thomas and W. Wester and R. C. Wolf and F. B. Abdalla and M. Banerji and A. Benoit-Lévy and E. Bertin and D. Brooks and A. Carnero Rosell and F. J. Castander and L. N. da Costa and R. Covarrubias and D. L. DePoy and S. Desai and H. T. Diehl and P. Doel and T. F. Eifler and A. Fausti Neto and D. A. Finley and B. Flaugher and P. Fosalba and J. Frieman and D. Gerdes and D. Gruen and R. A. Gruendl and D. James and K. Kuehn and N. Kuropatkin and O. Lahav and T. S. Li and M. A. G. Maia and M. Makler and M. March and J. L. Marshall and P. Martini and K. W. Merritt and R. Miquel and B. Nord and R. Ogando and A. A. Plazas and A. K. Romer and A. Roodman and E. Sanchez and V. Scarpine and M. Schubnell and I. Sevilla-Noarbe and R. C. Smith and M. Soares-Santos and F. Sobreira and E. Suchyta and M. E. C. Swanson and G. Tarle and J. Thaler and A. R. Walker},
journal= {arXiv preprint arXiv:1504.02936},
year = {2015}
}
Comments
24 pages, 9 figures, 4 tables v3: added link to training data / implementation