The sloan digital Sky Survey-II Supernova Survey: Search algorithm and follow-up observations

Masao Sako, Bruce Bassett, Andrew Becker, David Cinabro, Fritz Dejongh, D. L. Depoy, Ben Dilday, Mamoru Doi, Joshua A. Frieman, Peter M. Garnavich, Craig J. Hogan, Jon Holtzman, Saurabh Jha, Richard Kessler, Kohki Konishi, Hubert Lampeitl, John Marriner, Gajus Miknaitis, Robert C. Nichol, Jose Luis PrietoAdam G. Riess, Michael W. Richmond, Roger Romani, Donald P. Schneider, Mathew Smith, Mark Subbarao, Naohiro Takanashi, Kouichi Tokita, Kurt Van Der Heyden, Naoki Yasuda, Chen Zheng, John Barentine, Howard Brewington, Changsu Choi, Jack Dembicky, Michael Harnavek, Yutaka Ihara, Myungshin Im, William Ketzeback, Scott J. Kleinman, Jurek Krzesiński, Daniel C. Long, Elena Malanushenko, Viktor Malanushenko, Russet J. McMillan, Tomoki Morokuma, Atsuko Nitta, Kaike Pan, Gabrelle Saurage, Stephanie A. Snedden

Research output: Contribution to journalArticlepeer-review

178 Scopus citations

Abstract

The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 deg2 region along the celestial equator during its first two seasons of a three-season campaign. Multi-band (ugriz) light curves were measured for most of the sources, which include solar system objects, galactic variable stars, active galactic nuclei, supernovae (SNe), and other astronomical transients. The imaging survey is augmented by an extensive spectroscopic follow-up program to identify SNe, measure their redshifts, and study the physical conditions of the explosions and their environment through spectroscopic diagnostics. During the survey, light curves are rapidly evaluated to provide an initial photometric type of the SNe, and a selected sample of sources are targeted for spectroscopic observations. In the first two seasons, 476 sources were selected for spectroscopic observations, of which 403 were identified as SNe. For the type Ia SNe, the main driver for the survey, our photometric typing and targeting efficiency is 90%. Only 6% of the photometric SN Ia candidates were spectroscopically classified as non-SN Ia instead, and the remaining 4% resulted in low signal-to-noise, unclassified spectra. This paper describes the search algorithm and the software, and the real-time processing of the SDSS imaging data. We also present the details of the supernova candidate selection procedures and strategies for follow-up spectroscopic and imaging observations of the discovered sources.

Original languageEnglish (US)
Pages (from-to)348-373
Number of pages26
JournalAstronomical Journal
Volume135
Issue number1
DOIs
StatePublished - Jan 1 2008

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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