The Extended HST Supernova Survey: The Rate of SNe Ia at z>1.4 Remains Low

TL;DR

This study extends the measurement of Type Ia supernova rates to redshifts above 1.4, confirming a low rate at these high redshifts and supporting the idea of a significant delay time between star formation and supernova explosion.

Contribution

It provides the most extensive supernova rate measurement at z>1.4, supporting a long delay time and challenging simple bimodal delay time models.

Findings

Supernova rate drops at z>1.4

Delay time estimated at 2-3 Gyr

Results support long delay time hypothesis

Abstract

We use the HST ACS imaging of the two GOODS fields during Cycles 11, 12, and 13 to derive the Type Ia supernova rate in four redshift intervals in the range 0.2<z<1.8. Compared to our previous results based on Cycle 11 observations only, we have increased the coverage of the search by a factor 2.7, making the total area searched equivalent to one square degree. The sample now consists of 56 Type Ia supernovae. The rates we derive are consistent with our results based on the Cycle 11 observations. In particular, the few detected supernovae at z>1.4 supports our previous result that there is a drop in the Type Ia supernova rate at high redshift, suggesting a long time delay between the formation of the progenitor star and the explosion of the supernova. If described by a simple one-parameter model, we find a characteristic delay time of 2-3 Gyr. However, a number of recent results based on e.g., low redshift supernova samples and supernova host galaxy properties suggest that the supernova delay time distribution is bimodal. In this model, a major fraction of the Type Ia supernova rate is 'prompt' and follows the star formation rate, while a smaller fraction of the rate has a long delay time, making this channel proportional to mass. While our results are fully consistent with the bimodal model at low redshifts, the low rate we find at z>1.4 appears to contradict these results. Models that corrects for star formation hidden by dust may explain at least part of the differences. Here we discuss this possibility together with other ways to reconcile data and models.