Not color-blind: Using multiband photometry to classify supernovae

Large numbers of supernovae (SNe) have been discovered in recent years, and many more will be found in the near future. Once discovered, further study of a SN and its possible use as an astronomical tool (e.g., as a distance estimator) require knowledge of the SN type. Current classification methods rely almost solely on the analysis of SN spectra to determine their type. However, spectroscopy may not be possible or practical when SNe are faint, numerous, or discovered in archival studies. We present a classification method for SNe based on the comparison of their observed colors with synthetic ones, calculated from a large database of multiepoch optical spectra of nearby events. We discuss the capabilities and limitations of this method. For example, Type la SNe at redshifts z < 0.1 can be distinguished from most other SN types during the first few weeks of their evolution, based on V-R versus R-I colors. Type II-P SNe have distinct (very red) colors at late (t > 100 days) stages. Broadband photometry through standard Johnson-Cousins UBVRI filters can be useful to classify SNe out to z z approximate to 0.6. The use of Sloan Digital Sky Survey (SDSS) ugriz filters allows the extension of our classification method to even higher redshifts (z = 0.75), and the use of infrared bands, to z = 2.5. We demonstrate the application of this method to a recently discovered SN from the SDSS. Finally, we outline the observational data required to further improve the sensitivity of the method and discuss prospects for its use on future SN samples. Community access to the tools developed is provided by a dedicated Web site.(5)