PHOTOMETRIC TYPE Ia SUPERNOVA CANDIDATES FROM THE THREE-YEAR SDSS-II SN SURVEY DATA

Abstract

We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band 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 spectroscopicallyconfirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibit biases that require further investigation for precision cosmology.

Photometric properties of intermediate-redshift Type Ia supernovae observed by the Sloan Digital Sky Survey-II Supernova Survey

“…Since the SN candidates were selected based only on photometry at early phases, the probability of misidentification would be smaller than 10% for the photometrically probable SNe Ia with late epoch observations. Sako et al (2011) estimates purity of the photometrically probable SNe Ia is ∼91% and contamination of other type SN is ∼6%. So the number of those misidentified SN must be less than 7 in the SDSS sample (we have 71 photometrically probable SNe Ia at the redshift range).…”

“…Because of insufficient telescope time for spectroscopic observations of faint objects, we did not obtain spectra for many SNe, especially those at higher redshift. We did acquire light curves of many of those missed "SNe Ia", but we use only light curves which are classified as having a high probability of being a SN Ia (see Sako et al 2011). …”

“…As discussed in §2, we added spectroscopically or photometrically probable SNe Ia to the SDSS sample in order to reduce the biases described above and to preserve the diversity of the sample since spectroscopically confirmed SNe Ia are strongly biased (Sako et al 2011). However, the incorporation of photometrically probable SNe Ia may introduce contamination by other types of SNe.…”

Searching for the first stars with theGaiamission

“…Since we do not have any observation of these objects, we have to rely on theoretical models to compare the data. A way to look for such objects that is worth future investigation is the use of automatic light curve classifiers, which are widely implemented for classifying supernovae and transients in general (Johnson & Crotts 2006;Kuznetsova & Connolly 2007;Poznanski et al 2007;Rodney & Tonry 2009;Falck et al 2010;Newling et al 2011;Richards et al 2011;Sako et al 2011;Ishida & de Souza 2012). In principle, the theoretical model could work as a training set for the classifier, which would be then applied to surveys to identify possible candidates for further spectroscopical follow up.…”

Analytic photometric redshift estimator for Type Ia supernovae from the Large Synoptic Survey Telescope

“…However, the observed photometric SNe need to be classified first using photometry (see "Supernova Photometric Classification Challenge" by Kessler et al (2010b) for a detailed discussion). Even applying an optimized version of the top performing method from the Supernova Photometric Classification Challenge, the photometric-classification algorithm of Sako et al (2011) result in over 25% of the resultant photometric SN Ia sample remaining non-Ia SNe (Campbell et al 2013). 3 We will defer the difficult task of photometric classificationo future work, and focus on the relatively easier task of estimating redshifts of known SNe Ia using photometry only.…”