Inverting cosmic ray propagation by Convolutional Neural Networks

TL;DR

This paper introduces a machine learning approach using convolutional neural networks to efficiently invert cosmic ray propagation processes and estimate source and propagation parameters from spectral data, outperforming traditional methods.

Contribution

The paper presents a novel CNN-based method for inverting cosmic ray propagation, demonstrating its effectiveness and efficiency over traditional MCMC techniques.

Findings

CNNs accurately infer propagation parameters from spectral data.

The approach is more efficient than traditional MCMC fitting.

Networks can incorporate artificial fluctuations to improve robustness.

Abstract

We propose a machine learning method to investigate the propagation of cosmic rays based on the precisely measured spectra of the primary and secondary cosmic ray nuclei of Li, Be, B, C, and O from AMS-02, ACE, and Voyager-1. We train two convolutional neural networks. One network learns how to infer propagation and source parameters from the energy spectra of cosmic rays, and the other network, which is similar to the former, has the flexibility to learn from the data with added artificial fluctuations. Together with the simulated data generated by GALPROP, we find that both networks can properly invert the propagation process and infer the propagation and source parameters reasonably well. This approach can be much more efficient than the traditional Markov chain Monte Carlo fitting method for deriving the propagation parameters if users choose to update confidence intervals with new experimental data. Both of the trained networks are available at (https://github.com/alan200276/CR_ML).