Exotic energy injection with ExoCLASS: Application to the Higgs portal model and evaporating black holes

TL;DR

This paper introduces ExoCLASS, a new tool for analyzing the impact of exotic electromagnetic energy injections, like evaporating black holes, on the CMB, providing robust bounds and improving detection prospects.

Contribution

We developed a user-friendly extension of CLASS to accurately model exotic energy injections and applied it to constrain primordial black holes and Higgs portal dark matter models.

Findings

Validated the tool on Higgs portal WIMP scenario.

Derived strong CMB bounds on primordial black hole relic density.

Future experiments could significantly enhance sensitivity.

Abstract

We devise a new user-friendly tool interfaced with the Boltzmann code CLASS to deal with any kind of exotic electromagnetic energy injection in the universe and its impact on anisotropies of the Cosmic Microwave Background. It makes use of the results from standard electromagnetic cascade calculations develop in the context of WIMP annihilation, generalized to incorporate any injection history. We first validate it on a specific WIMP scenario, the Higgs Portal model, confirming that the standard effective on-the-spot treatment is accurate enough. We then analyze the more involved example of evaporating Primordial Black Holes (PBHs) with masses in the range $[3\times10^{13},5\times10^{16}]$g, for which the standard approximations break down. We derive robust CMB bounds on the relic density of evaporating PBHs, ruling out the possibility for PBHs with a monochromatic distribution of masses in the range $[3\times10^{13},2.5\times10^{16}]$g to represent all of the Dark Matter in our Universe. Remarkably, we confirm with an accurate study that the CMB bounds are several orders of magnitude stronger than those from the galactic gamma-ray background in the range $[3\times10^{13},3\times10^{14}]$g. A future CMB experiment like CORE+, or an experiment attempting at measuring the 21 cm signal from the Dark Ages could greatly improve the sensitivity to these models.