Photometric type Ia supernova candidates from the three-year SDSS-II SN survey data

Masao Sako, Bruce Bassett, Brian Connolly, Benjamin Dilday, Heather Cambell, Joshua A. Frieman, Larry Gladney, Richard Kessler, Hubert Lampeitl, John Marriner, Ramon Miquel, Robert C. Nichol, Donald P. Schneider, Mathew Smith, Jesper Sollerman

Research output: Contribution to journalArticlepeer-review

87 Scopus citations

Abstract

We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with 91% efficiency and with a contamination of 6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only 20%-40% larger than that of the spectroscopically confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibits biases that require further investigation for precision cosmology.

Original languageEnglish (US)
Article number162
JournalAstrophysical Journal
Volume738
Issue number2
DOIs
StatePublished - Sep 10 2011

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Fingerprint Dive into the research topics of 'Photometric type Ia supernova candidates from the three-year SDSS-II SN survey data'. Together they form a unique fingerprint.

Cite this