Extending Supernova Spectral Templates for Next-Generation Space Telescope Observations

TL;DR

This paper develops an extended library of supernova spectral energy distribution templates covering UV to IR wavelengths, crucial for future space telescope surveys like JWST and WFIRST, and provides tools for their generation.

Contribution

It introduces a new repository of empirically extended supernova SED templates into UV and IR, enhancing modeling for upcoming space-based SN observations.

Findings

Extended SED templates improve supernova classification at UV and IR wavelengths.

The repository supports Type Ia, Ib, Ic, and II supernova models.

Open-source Python tool enables custom SED extrapolations.

Abstract

Empirical models of supernova (SN) spectral energy distributions (SEDs) are widely used for SN survey simulations and photometric classifications. The existing library of SED models has excellent optical templates but limited, poorly constrained coverage of ultraviolet (UV) and infrared (IR) wavelengths. However, both regimes are critical for the design and operation of future SN surveys, particularly at IR wavelengths that will be accessible with the James Webb Space Telescope (JWST) and the Wide-Field Infrared Survey Telescope (WFIRST). We create a public repository of improved empirical SED templates using a sampling of Type Ia and core-collapse (CC) photometric light curves to extend the Type Ia parameterized SALT2 model and a set of SN Ib, SN Ic, and SN II SED templates into the UV and near-IR. We apply this new repository of extrapolated SN SED models to examine how future surveys can discriminate between CC and Type Ia SNe at UV and IR wavelengths, and present an open-source software package written in Python, SNSEDextend, that enables a user to generate their own extrapolated SEDs.