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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.
The GAPS experiment is foreseen to carry out a dark matter search by measuring low-energy cosmic-ray antideuterons and antiprotons with a novel detection approach. It will provide a new avenue to access a wide range of different dark matter models an
Global fits of primary and secondary cosmic-ray (CR) fluxes measured by AMS-02 have great potential to study CR propagation models and search for exotic sources of antimatter such as annihilating dark matter (DM). Previous studies of AMS-02 antiproto
The direct detection of particle dark matter through its scattering with nucleons is of fundamental importance to understand the nature of DM. In this work, we propose that the high-energy neutrino detectors like IceCube can be used to uniquely probe
Cosmic-ray antiprotons are a powerful tool for astroparticle physics. While the bulk of measured antiprotons is consistent with a secondary origin, the precise data of the AMS-02 experiment provides us with encouraging prospects to search for a subdo
Cosmological simulations play an important role in the interpretation of astronomical data, in particular in comparing observed data to our theoretical expectations. However, to compare data with these simulations, the simulations in principle need t