The High Cadence Transit Survey (HiTS): Compilation and Characterization of Light-curve Catalogs

Indexación: Scopus.J.M. acknowledges support from CONICYT-Chile through CONICYT-PCHA/Doctorado-Nacional/2014-21140892. J.M., F.F., G.C.V., and G.M. acknowledge support from the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009, awarded to the Millennium Institute of Astrophysics (MAS). F.F. acknowledges support from Conicyt through the Fondecyt Initiation into Research project No. 11130228. J.M., F.F., J.S.M., G.C.V., and S.G. acknowledge support from Basal Project PFB-03, Centro de Modelamiento Matemáico (CMM), Universidad de Chile. P.L. acknowledges support by Fondecyt through project #1161184. G.C.V. gratefully acknowledges financial support from CON-ICYT-Chile through FONDECYT postdoctoral grant number 3160747 and CONICYT-Chile and NSF through the Programme of International Cooperation project DPI201400090. P.H. acknowledges support from FONDECYT through grant 1170305. L.G. was supported in part by the US National Science Foundation under grant AST-1311862. G.M. acknowledges support from Conicyt through CONICYT-PCHA/Magís-terNacional/2016-22162353. Support for T.d.J. has been provided by US NSF grant AST-1211916, the TABASGO Foundation, and Gary and Cynthia Bengier. R.R.M. acknowledges partial support from BASAL Project PFB-06, as well as FONDECYT project N◦1170364. Powered@NLHPC: this research was supported by the High Performance Computing infrastructure of the National Laboratory for High Performance Computing (NLHPC), PIA ECM-02, CONICYT. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaborating institutions: Argonne National Lab, the University of California Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologi-cas-Madrid, the University of Chicago, University College London, the DES-Brazil consortium, the University of Edinburgh, ETH-Zurich, the University of Illinois at Urbana-Champaign, Institut de Ciencies de l’Espai, Institut de Fisica d’Altes Energies, Lawrence Berkeley National Lab, Ludwig-Maximilians Universitat, the University of Michigan, the National Optical Astronomy Observatory, the University of Nottingham, Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Lab, Stanford University, the University of Sussex, and Texas A&M University. Funding for DES, including DECam, has been provided by the U.S. Department of Energy, National Science Foundation, Ministry of Education and Science (Spain), Science and Technology Facilities Council (UK), Higher Education Funding Council (England), National Center for Supercomputing Applications, Kavli Institute for Cosmological Physics, Financia-dora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo a Pesquisa, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência e Tecnologia (Brazil), the German Research Foundation-sponsored cluster of excellence “Origin and Structure of the universe,” and the DES collaborating institutions. Facility: CTIO:1.5 m (DECam).The High Cadence Transient Survey (HiTS) aims to discover and study transient objects with characteristic timescales between hours and days, such as pulsating, eclipsing, and exploding stars. This survey represents a unique laboratory to explore large etendue observations from cadences of about 0.1 days and test new computational tools for the analysis of large data. This work follows a fully data science approach, from the raw data to the analysis and classification of variable sources. We compile a catalog of ∼15 million object detections and a catalog of ∼2.5 million light curves classified by variability. The typical depth of the survey is 24.2, 24.3, 24.1, and 23.8 in the u, g, r, and i bands, respectively. We classified all point-like nonmoving sources by first extracting features from their light curves and then applying a random forest classifier. For the classification, we used a training set constructed using a combination of cross-matched catalogs, visual inspection, transfer/active learning, and data augmentation. The classification model consists of several random forest classifiers organized in a hierarchical scheme. The classifier accuracy estimated on a test set is approximately 97%. In the unlabeled data, 3485 sources were classified as variables, of which 1321 were classified as periodic. Among the periodic classes, we discovered with high confidence one δ Scuti, 39 eclipsing binaries, 48 rotational variables, and 90 RR Lyrae, and for the nonperiodic classes, we discovered one cataclysmic variable, 630 QSOs, and one supernova candidate. The first data release can be accessed in the project archive of HiTS (http://astro.cmm.uchile.cl/HiTS/). © 2018. The American Astronomical Society. All rights reserved.https://iopscience.iop.org/article/10.3847/1538-3881/aadfd