Type-Ia supernovae: new clues to their progenitors from the delay-time distribution

TL;DR

Recent progress in reconstructing the delay-time distribution of type-Ia supernovae suggests a power-law shape supporting the double-degenerate progenitor model, but uncertainties and model discrepancies remain.

Contribution

The paper reviews recent measurements of the SN Ia delay-time distribution, highlighting its shape and implications for progenitor models, especially the double-degenerate scenario.

Findings

The DTD follows a 1/t power-law for 1<t<10 Gyr.

The DTD peaks at the shortest delays but shape uncertainties exist between 0.1 and 1 Gyr.

Binary population synthesis models need to increase predicted rates by factors of 3-10.

Abstract

Despite their prominent role in cosmography, little is yet known about the nature of type-Ia supernovae (SNe Ia), from the identity of their progenitor systems, through the evolution of those systems up to ignition and explosion, and to the causes of the environmental dependences of their observed properties. I briefly review some of those puzzles. I then focus on recent progress in reconstructing the SN Ia delay time distribution (DTD) -- the SN rate versus time that would follow a hypothetical brief burst of star formation. A number of measurements of the DTD over the past two years, using different methods and based on SNe Ia in different environments and redshift ranges, are converging. At delays 1<t<10 Gyr, these measurements show a similar 1/t power-law shape, with similar normalizations. The DTD peaks at the shortest delays probed, but there is still some uncertainty regarding its precise shape in the range 0.1<t<1 Gyr. At face value, this result supports Ron Webbink's (1984) idea of a double-degenerate progenitor origin for SNe Ia, but the numbers currently predicted by binary population synthesis models must be increased by factors of 3-10, at least. Single-degenerate progenitors may still play a role in producing short-delay SNe Ia, or perhaps all SNe Ia, if there is something important missing in the current modeling efforts.