Locating Type Ia Supernovae in HST Archival Data via an Artifical Neural Network
Abstract
The rate of type Ia supernovae (SNe Ia) in the early universe puts important constraints on the nature of SN Ia progenitors, and had implications on dark energy. The precise limits on these rates are challenged by etendue and resolution factors which make real time investigations largely impractical, and the limited "per event" information which make archival studies seemingly inconceivable. There is, however, a wealth of information on high-redshift (z > 1) events from the GOODS, CANDELS, and other HST SN surveys, largely based on brightness constraints in relation to their host galaxy characteristics, that put high-z SNe Ia in a somewhat unique (and identifiable) parameter space. We describe our program to map these observed characteristics of SNe Ia and their host galaxies at z > 1 with artificial neural networks, and in turn use these trained networks to probabilistically locate undiscovered SNe Ia in MAST using the developing Hubble Source Catalog. We expect that the orders of magnitude increase in survey area will lead to a more statistically definitive sample, determining the exact trend in the cosmic SN Ia rate history in this crucial epoch.
- Publication:
-
American Astronomical Society Meeting Abstracts #225
- Pub Date:
- January 2015
- Bibcode:
- 2015AAS...22514014S