The Sloan Digital Sky Survey-II Supernova Survey: search Algorithm and follow-up observations
Date
2008-01Author
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
Metadata
Show full item recordAbstract
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.