Supernova Photometric Classification Challenge

TL;DR

This paper introduces a public challenge for classifying supernovae types using simulated data modeled after the Dark Energy Survey, aiming to evaluate and improve classification algorithms.

Contribution

It provides a blinded, realistic supernova dataset for algorithm testing and assesses the effectiveness of different classification methods.

Findings

Evaluation of various classification algorithms' strengths and weaknesses

Insights into the amount of spectroscopic data needed for training

Identification of optimal strategies for supernova type classification

Abstract

We have publicly released a blinded mix of simulated SNe, with types (Ia, Ib, Ic, II) selected in proportion to their expected rate. The simulation is 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 Simulations of non-Ia type SNe are based on spectroscopically confirmed light curves that include unpublished non-Ia samples donated from the Carnegie Supernova Project (CSP), the Supernova Legacy Survey (SNLS), and the Sloan Digital Sky Survey-II (SDSS-II). We challenge scientists to run their classification algorithms and report a type for each SN. A spectroscopically confirmed subset is provided for training. The goals of this challenge are to (1) learn the relative strengths and weaknesses of the different classification algorithms, (2) use the results to improve classification algorithms, and (3) understand what spectroscopically confirmed sub-sets are needed to properly train these algorithms. The challenge is available at www.hep.anl.gov/SNchallenge, and the due date for classifications is May 1, 2010.