Constraining dark matter annihilation with cosmic ray antiprotons using neural networks
This work addresses the bottleneck of slow simulations in dark matter research, allowing for more efficient analysis of indirect detection data, though it is incremental as it improves existing methods rather than introducing a new paradigm.
The paper tackled the computational expense of simulating cosmic-ray propagation for dark matter detection by developing a Recurrent Neural Network method that accelerates simulations by at least two orders of magnitude while maintaining accuracy, enabling efficient parameter scans for dark matter models using AMS-02 antiproton data.
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.