A New Statistical Model for Population III Supernova Rates: Discriminating Between $\Lambda$CDM and WDM Cosmologies

TL;DR

This paper introduces a semi-analytical model to estimate Population III supernova rates, demonstrating how early supernova observations can distinguish between different dark matter models and inform survey strategies.

Contribution

The study presents a new, efficient statistical model that accurately reproduces supernova rates from simulations and explores its potential to differentiate dark matter cosmologies.

Findings

Model reproduces supernova rates well

Supernova observations can distinguish dark matter types

Predictions aid in optimizing survey strategies

Abstract

With new observational facilities becoming available soon, discovering and characterising supernovae from the first stars will open up alternative observational windows to the end of the cosmic dark ages. Based on a semi-analytical merger tree model of early star formation we constrain Population III supernova rates. We find that our method reproduces the Population III supernova rates of large-scale cosmological simulations very well. Our computationally efficient model allows us to survey a large parameter space and to explore a wide range of different scenarios for Population III star formation. Our calculations show that observations of the first supernovae can be used to differentiate between cold and warm dark matter models and to constrain the corresponding particle mass of the latter. Our predictions can also be used to optimize survey strategies with the goal to maximize supernova detection rates.