Constraining dark matter annihilation with cosmic ray antiprotons using neural networks

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.

Investigation of the relation between space-weather parameters and Forbush decreases automatically selected from Moscow and Apatity cosmic ray stations during solar cycle 23

We present the results of an investigation of the relation between space-weather parameters and cosmic ray (CR) intensity modulation using algorithm-selected Forbush decreases (FDs) from Moscow (MOSC) and Apatity (APTY) neutron monitor (NM) stations during solar cycle 23. Our FD location program detected 408 and 383 FDs from MOSC and APTY NM stations respectively. A coincident computer code employed in this work detected 229 FDs that were observed at the same Universal Time (UT) at the two stations. Out of the 229 simultaneous FDs, we formed a subset of 139 large FDs(%) ≤ − 4 at the MOSC station. We performed a two-dimensional regression analysis between the FD magnitudes and the space-weather data on the two samples. We find that there were significant space-weather disturbances at the time of the CR flux depressions. The correlation between the space-weather parameters and decreases in galactic cosmic ray (GCR) intensity at the two NM stations is statistically significant. The implications of the present space-weather data on CR intensity depressions are highlighted.