The Sloan Digital Sky Survey-Ii Supernova Survey: Search Algorithm and Follow-Up Observations

Abstract

The Sloan Digital Sky Survey-II Supernova Survey has identified a large number of new transient sources in a 300 deg 2 region along the celestial equator during its first two seasons of a three-season campaign. Multiband (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 realtime 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.

Observational probes of cosmic acceleration

“…At very high redshifts, HST surveys (Riess et al, 2004Suzuki et al, 2012) have yielded ∼ 25 Type Ia SNe at z > 1.0, which confirm the expectation that the universe was decelerating at high redshift and limit possible systematic effects from evolution of the supernova population or intergalactic dust extinction. At intermediate redshifts (0.1 < z < 0.4), the SDSS-II supernova survey Sako et al, 2008) has discovered and monitored 500 spectroscopically confirmed Type Ia SNe; only the first-year data set (103 SNe) has so far been subjected to a full cosmological analysis (Kessler et al, 2009 present cosmological results from a sample of 752 photometrically classified SDSS-II SNe with spectroscopic host galaxy redshifts, and a joint analysis of the SNLS and SDSS-II samples is in process (J. Frieman, private communication). Finally, the last five years have also seen major efforts to expand the sample of local calibrators and improve their measurements, including rest-frame IR and rest-frame UV photometry (Wood-Vasey et al, 2008;Stritzinger et al, 2011;Contreras et al, 2010;Hicken et al, 2009a).…”

MEASUREMENTS OF THE RATE OF TYPE Ia SUPERNOVAE AT REDSHIFT ≲0.3 FROM THE SLOAN DIGITAL SKY SURVEY II SUPERNOVA SURVEY

“…Much of the material in this section is also relevant to the SN rate studies described by Dilday et al (2010), and is discussed more fully therein. The survey is described in more detail by Frieman et al (2008), and the SN detection algorithms are given by Sako et al (2008). Additional details of the survey observations and the use of in situ artificial SNe for determining SN detection efficiencies are discussed by Dilday et al (2008).…”

“…As a key component of prioritizing SN candidates for follow-up spectroscopic observations, the light curves of SN candidates were fit to models of Type Ia, Type Ib/c, and Type II SNe (see Filippenko 1997 for a review of SN types). This procedure is referred to as "photometric typing," and is described in detail by Sako et al (2008).…”

“…Some core-collapse SNe are well fit by the broad light curve of SN 2005gj, and this cut also serves as a way for rejecting these from our SN Ia sample selection. Search-discovery epochs refer to epochs for which the transient object was detected by the survey difference imaging and object detection pipeline (Sako et al 2008).…”

Sunfall: A Collaborative Visual Analytics System for Astrophysics

“…In 2005, the SNfactory pioneered the use of machine learning algorithms to replace "cuts" for supernova object detection [9], and since then, such techniques have been utilized by other supernova searches, including SDSS and SNLS [16,17]. The SDSS "autoscanner" uses a combination of heuristics and a naive Bayes classifier to filter out non-SN objects, but they do not provide a comparison of the efficacy of any other types of machine learning algorithms for supernova search.…”