$\overline{\text{D}}$arkRayNet: Emulation of cosmic-ray antideuteron fluxes from dark matter

TL;DR

This paper introduces DarkRayNet, a neural network emulator that rapidly predicts cosmic-ray antideuteron fluxes from dark matter annihilation, accounting for uncertainties in cosmic-ray propagation and coalescence, and assesses experimental sensitivities.

Contribution

The paper presents DarkRayNet, a novel neural network emulator for antideuteron flux prediction, incorporating updated coalescence and propagation models, and evaluates dark matter detection prospects.

Findings

DarkRayNet enables fast flux predictions across various models.

AMS-02 is sensitive mainly below 20 GeV dark matter masses.

GAPS can independently probe the low-mass dark matter region.

Abstract

Cosmic-ray antimatter, particularly low-energy antideuterons, serves as a sensitive probe of dark matter annihilating in our Galaxy. We study this smoking-gun signature and explore its complementarity with indirect dark matter searches using cosmic-ray antiprotons. To this end, we develop the neural network emulator $\overline{\text{D}}$arkRayNet, enabling a fast prediction of propagated antideuteron energy spectra for a wide range of annihilation channels and their combinations. We revisit the Monte Carlo simulation of antideuteron coalescence and cosmic-ray propagation, allowing us to explore the uncertainties of both processes. In particular, we take into account uncertainties from the $\Lambda_b$ production rate and consider two distinctly different propagation models. Requiring consistency with cosmic-ray antiproton limits, we find that AMS-02 shows sensitivity to a few windows of dark matter masses only, most prominently below 20 GeV. This region can be probed independently by the upcoming GAPS experiment. The program package $\overline{\text{D}}$arkRayNet is available on GitHub.