TY - JOUR
T1 - Identifying Tidal Disruption Events via Prior Photometric Selection of Their Preferred Hosts
AU - French, K. Decker
AU - Zabludoff, Ann I.
N1 - The Pan-STARRS1 Surveys (PS1) have been made possible through contributions of the Institute for Astronomy, the University of Hawaii, the Pan-STARRS Project Office, the Max Planck Society and its participating institutes, the Max Planck Institute for Astronomy, Heidelberg and the Max Planck Institute for Extraterrestrial Physics, Garching, The Johns Hopkins University, Durham University, the University of Edinburgh, Queen’s University Belfast, the Harvard-Smithsonian Center for Astrophysics, the Las Cumbres Observatory Global Telescope Network Incorporated, the National Central University of Taiwan, the Space Telescope Science Institute, the National Aeronautics and Space Administration under grant No. NNX08AR22G issued through the Planetary Science Division of the NASA Science Mission Directorate, the National Science Foundation under grant No. AST-1238877, the University of Maryland, and Eotvos Lorand University (ELTE).
We thank the referee for useful comments that have improved this manuscript. We thank Daniel Eisenstein for his motivating question about post-starburst selection in photometric surveys. K.D.F. is supported by Hubble Fellowship grant HST-HF2-51391.001-A, provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. A.I.Z. acknowledges funding from NSF grant AST-0908280 and NASA grant ADP-NNX10AE88G.
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 website ishttp://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration, including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University, University of Florida, the French Participation Group, the German Participation Group, Harvard University, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/ JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, the Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.
This project used public archival data from the Dark Energy Survey (DES). Funding for the DES Projects has been provided by the DOE and NSF (USA), MISE (Spain), STFC (UK), HEFCE (UK), NCSA (UIUC), KICP (U. Chicago), CCAPP (Ohio State), MIFPA (Texas A&M), CNPQ, FAPERJ, FINEP (Brazil), MINECO (Spain), DFG (Germany) and the collaborating institutions in the Dark Energy Survey, which are Argonne Lab, UC Santa Cruz, University of Cambridge, CIEMAT-Madrid, University of Chicago, University College London, DES-Brazil Consortium, University of Edinburgh, ETH Zurich, Fermilab, University of Illinois, ICE (IEEC-CSIC), IFAE Barcelona, Lawrence Berkeley Lab, LMU Munchen and the associated Excellence Cluster Universe, University of Michigan, NOAO, University of Nottingham, the Ohio State University, OzDES Membership Consortium, University of Pennsylvania, University of Portsmouth, SLAC National Lab, Stanford University, University of Sussex, and Texas A&M University. Based in part on observations at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - A nuclear transient detected in a post-starburst galaxy or other quiescent galaxy with strong Balmer absorption is likely to be a tidal disruption event (TDE). Identifying such galaxies within the planned survey footprint of the Large Synoptic Survey Telescope (LSST) before a transient is detected will make TDE classification immediate and follow-up more efficient. Unfortunately, spectra for identifying most such galaxies are unavailable, and simple photometric selection is ineffective; cutting on "green valley" UV/optical/IR colors produces samples that are highly contaminated and incomplete. Here we propose a new strategy using only photometric optical/UV/IR data from large surveys. Applying a machine-learning random forest classifier to a sample of ∼400,000 SDSS galaxies with Galaxy Evolution Explorer (GALEX) and Wide-field Infrared Survey Explorer (WISE) photometry, including 13,592 quiescent Balmer-strong galaxies, we achieve 53%-61% purity and 8%-21% completeness, given the range in redshift. For the subset of 1299 post-starburst galaxies, we achieve 63%-73% purity and 5%-12% completeness. Given these results, the range of likely TDE and supernova rates, and that 36%-75% of TDEs occur in quiescent Balmer-strong hosts, we estimate that 13%-99% of transients observed in photometrically selected host galaxies will be TDEs and that we will discover 119-248 TDEs per year with LSST. Using our technique, we present a new catalog of 67,484 candidate galaxies expected to have a high TDE rate, drawn from the SDSS, Pan-STARRS, DES, and WISE photometric surveys. This sample is 3.5× larger than the current SDSS sample of similar galaxies, thereby providing a new path forward for transient science and galaxy evolution studies.
AB - A nuclear transient detected in a post-starburst galaxy or other quiescent galaxy with strong Balmer absorption is likely to be a tidal disruption event (TDE). Identifying such galaxies within the planned survey footprint of the Large Synoptic Survey Telescope (LSST) before a transient is detected will make TDE classification immediate and follow-up more efficient. Unfortunately, spectra for identifying most such galaxies are unavailable, and simple photometric selection is ineffective; cutting on "green valley" UV/optical/IR colors produces samples that are highly contaminated and incomplete. Here we propose a new strategy using only photometric optical/UV/IR data from large surveys. Applying a machine-learning random forest classifier to a sample of ∼400,000 SDSS galaxies with Galaxy Evolution Explorer (GALEX) and Wide-field Infrared Survey Explorer (WISE) photometry, including 13,592 quiescent Balmer-strong galaxies, we achieve 53%-61% purity and 8%-21% completeness, given the range in redshift. For the subset of 1299 post-starburst galaxies, we achieve 63%-73% purity and 5%-12% completeness. Given these results, the range of likely TDE and supernova rates, and that 36%-75% of TDEs occur in quiescent Balmer-strong hosts, we estimate that 13%-99% of transients observed in photometrically selected host galaxies will be TDEs and that we will discover 119-248 TDEs per year with LSST. Using our technique, we present a new catalog of 67,484 candidate galaxies expected to have a high TDE rate, drawn from the SDSS, Pan-STARRS, DES, and WISE photometric surveys. This sample is 3.5× larger than the current SDSS sample of similar galaxies, thereby providing a new path forward for transient science and galaxy evolution studies.
KW - galaxies: active
KW - galaxies: evolution
KW - methods: observational
UR - http://www.scopus.com/inward/record.url?scp=85057585374&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057585374&partnerID=8YFLogxK
U2 - 10.3847/1538-4357/aaea64
DO - 10.3847/1538-4357/aaea64
M3 - Article
AN - SCOPUS:85057585374
SN - 0004-637X
VL - 868
JO - Astrophysical Journal
JF - Astrophysical Journal
IS - 2
M1 - 99
ER -