Constraining light dark matter in vector-scalar portals with COSI and AMEGO-X

TL;DR

This paper explores how upcoming gamma-ray observatories like COSI and AMEGO-X can detect or constrain sub-GeV dark matter models involving vector-scalar portals, focusing on gamma-ray lines and continuum signals.

Contribution

It introduces a vector-scalar portal dark matter model at MeV scale and estimates the sensitivities of COSI and AMEGO-X to this scenario, highlighting their potential to probe new parameter space.

Findings

COSI can provide leading constraints beyond CMB limits.

AMEGO-X can probe most of the viable parameter space for continuum gamma rays.

The model implementation is available on GitHub.

Abstract

Detecting gamma-ray signals that could be due to dark matter (DM) particles would give us invaluable information about the nature of DM. In particular, gamma-ray lines could provide a way to measure the DM mass. The excellent energy resolution of the upcoming Compton Spectrometer and Imager (COSI) will allow us to probe underexplored regions of the DM parameter space while being sensitive to distinctive spectral features of potential DM signals. In this work, we consider a fermionic sub-GeV DM charged under a new U(1) gauge symmetry. Both the DM and the new gauge boson $Z'$ acquire mass from a new singlet scalar. The masses of the new particles in this class of vector-scalar portal models are naturally at the MeV scale, enabling detectable gamma-ray lines in the bandpasses of COSI and proposed missions such as the All-sky Medium Energy Gamma-ray Observatory eXplorer (AMEGO-X). We estimate the sensitivities of COSI and AMEGO-X to sub-GeV DM in this context, considering a B-L and a purely axial $Z'$ as benchmark examples. We find regions of the parameter space where COSI will provide leading constraints, beyond the strong CMB limits. On the other hand, AMEGO-X would probe most of the viable parameter space leading to continuum gamma rays. The implementation of our generic vector-scalar portal model in the Hazma toolkit is available at GitHub.