Results from the Supernova Photometric Classification Challenge

By R. Kessler, Bruce A. Bassett, P. Belov, Vasudha Bhatnagar, H. Campbell, A. Conley, Joshua A. Frieman, A. Glazov, S. González-Gaitán, R. Hložek and 17 more. 2010

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We report results from the Supernova Photometric Classification Challenge (SNPhotCC), a publicly released mix of simulated supernovae (SNe), with types (Ia, Ibc, and II) selected in proportion to their expected rates. The simulation was realized in the griz filters of the Dark Energy Survey (DES) with realistic observing conditions (sky noise, point-spread function, and atmospheric transparency) based on years of recorded conditions at the DES site. Simulations of non–Ia-type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). A spectroscopically confirmed subset was provided for training. We challenged scientists to run their classification algorithms and report a type and photo- z for each SN. Participants from 10 groups contributed 13 entries for the sample that included a host-galaxy photo- z for each SN and nine entries for the sample that had no redshift information. Several different classification strategies resulted in similar performance, and for all entries the performance was significantly better for the training subset than for the unconfirmed sample. For the spectroscopically unconfirmed subset, the entry with the highest average figure of merit for classifying SNe Ia has an efficiency of 0.96 and an SN Ia purity of 0.79. As a public resource for the future development of photometric SN classification and photo- z estimators, we have released updated simulations with improvements based on our experience from the SNPhotCC, added samples corresponding to the Large Synoptic Survey Telescope (LSST) and the SDSS-II, and provided the answer keys so that developers can evaluate their own analysis.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/064cf9b8e1634c02e25d116992fb85f0a337ebb0

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Citations in the corpus

Measuring Dark Energy Properties with Photometrically Classified Pan-STARRS Supernovae. II. Cosmological Parameters (2017, methodology): “We use the SNANA software (Kessler et al. 2010) to simulate SNe Ia based on the SALT2 model, with detection efficiencies, zeropoints, PSF sizes, sky noise, and other observables from the real PS1 and low-z surveys.”
Observational probes of cosmic acceleration (2012, background): “With good photometric monitoring and with subsequent spectroscopic redshifts of apparent hosts, Kessler et al. (2010) find that they can identify Type Ia SNe with 70% to 90% confidence from the LCS and color alone, and Bernstein et al. (2012) forecast Type Ia purity as high as 98% for DES-like…”
THE DIFFERENCE IMAGING PIPELINE FOR THE TRANSIENT SEARCH IN THE DARK ENERGY SURVEY (2015, methodology): “We fit both the data and MC samples with the SALT-II model (Guy et al. 2010) using the photo-z technique described in Kessler et al. (2010b).”
SUPERNOVA SIMULATIONS AND STRATEGIES FOR THE DARK ENERGY SURVEY (2011, methodology): “…above, with a range of ×3 in total sample SNcc. Note that the sample purity is better than that obtained with the same analysis performed in Kessler et al. (2010b); this is due to the correction for Malmquist bias applied to the core collapse simulation sample, which reduces their…”
The Data Release of the Sloan Digital Sky Survey-II Supernova Survey (2014, methodology): “We simulated 10 seasons worth of SN candidates using a mix of SN Ia, SN Ib/c, and SN II identical to that used in the SN Classification Challenge (Kessler et al. 2010b,c).”
PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING (2016, methodology): “The training set from the SPCC was known to be biased in the way spectroscopic followup datasets usually are, in that only the brightest objects tend to be followed up due to telescope time constraints and the difficulty in obtaining good spectra.”
SNLS3: CONSTRAINTS ON DARK ENERGY COMBINING THE SUPERNOVA LEGACY SURVEY THREE-YEAR DATA WITH OTHER PROBES (2011, methodology): “Spectroscopic follow-up is used to confirm SN types and measure redshifts, critical in obtaining clean samples of SNe Ia as reliable photometric identification techniques have yet to be developed, despite recent progress (Kessler et al. 2010; Bazin et al. 2011).”