The Sloan Digital Sky Survey-II Supernova Survey: search Algorithm and follow-up observations

Show simple item record Sako, Masao Bassett, Bruce Becker, Andrew Cinabro, David DeJongh, Fritz Depoy, Darren Dilday, Ben Doi, Mamoru Frieman, Joshua Garnavich, Peter Hogan, Craig Holtzman, Jon Jha, Saurabh Kessler, Richard Konishi, Kohki Lampeitl, Hubert Marriner, John Miknaitis, Gajus Nichol, Robert Prieto, Jose Luis Riess, Adam Richmond, Michael Romani, Roger Schneider, Donald Smith, Matthew SubbaRao, Mark Takanashi, Naohiro Tokita, Kouichi van der Heyden, Kurt Yasuda, Naoki Zheng, Chen Barentine, John Brewington, Howard Choi, Changsu Dembicky, Jack Harnavek, Michael Ihara, Yutaka Im, Myungshin Ketzeback, William Kleinman, Scott Krzesinski, Jurek Long, Daniel Malanushenko, Elena Malanushenko, Viktor McMillan, Russet Morokuma, Tomoki Nitta, Atsuko Pan, Kaike Saurage, Gabrelle Snedden, Stephani 2008-11-17T17:43:41Z 2008-11-17T17:43:41Z 2008-01
dc.identifier.citation The Astronomical Journal 135 (2008) 348-373 en_US
dc.description RIT community members may access full-text via RIT Libraries licensed databases:
dc.description.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. en_US
dc.language.iso en_US en_US
dc.publisher American Astronomical Society - The Astronomical Journal en_US
dc.relation.ispartofseries vol. 135 en_US
dc.relation.ispartofseries no. 1 en_US
dc.relation.ispartofseries pps. 348-373 en_US
dc.subject Cosmology - observations en_US
dc.subject Methods - data analysis, tech en_US
dc.title The Sloan Digital Sky Survey-II Supernova Survey: search Algorithm and follow-up observations en_US
dc.type Article en_US

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