Constraining dark matter annihilation with cosmic ray antiprotons using neural networks

TL;DR

This paper introduces a neural network-based method that accelerates cosmic ray antiproton simulations, enabling efficient dark matter model analysis with high accuracy and significantly reduced computational time.

Contribution

The authors develop a Recurrent Neural Network approach that drastically speeds up cosmic ray antiproton simulations, facilitating extensive dark matter parameter scans.

Findings

Achieves over 100x speed-up in simulations

Provides constraints on dark matter models using AMS-02 data

Released DarkRayNet for public use

Abstract

The interpretation of data from indirect detection experiments searching for dark matter annihilations requires computationally expensive simulations of cosmic-ray propagation. In this work we present a new method based on Recurrent Neural Networks that significantly accelerates simulations of secondary and dark matter Galactic cosmic ray antiprotons while achieving excellent accuracy. This approach allows for an efficient profiling or marginalisation over the nuisance parameters of a cosmic ray propagation model in order to perform parameter scans for a wide range of dark matter models. We identify importance sampling as particularly suitable for ensuring that the network is only evaluated in well-trained parameter regions. We present resulting constraints using the most recent AMS-02 antiproton data on several models of Weakly Interacting Massive Particles. The fully trained networks are released as DarkRayNet together with this work and achieve a speed-up of the runtime by at least two orders of magnitude compared to conventional approaches.