Deep Learning Analysis of Ions Accelerated at Shocks

TL;DR

This paper demonstrates how deep learning can classify ions accelerated at shocks and predict particle injection with high accuracy, offering new tools for analyzing kinetic plasma simulations.

Contribution

It introduces deep learning models for classifying ion acceleration regimes and predicting particle injection using magnetic field time series in shock simulations.

Findings

High accuracy (>90%) in predicting particle injection.

Autoencoder models successfully reconstruct parameter time series.

Potential for developing sub-grid models in fluid plasma simulations.

Abstract

We study the application of deep learning techniques to the analysis and classification of ions accelerated at collisionless shocks in hybrid (kinetic ions--fluid electrons) simulations. Ions were classified as thermal, suprathermal, or nonthermal, depending on the energy they achieved and the acceleration regime they fell under. These classifications were used to train deep learning models to predict which particles are injected into the acceleration process with high accuracy (>90%), using only time series of the local magnetic field they experienced during their initial interaction with the shock. An autoencoder architecture was also tested, for which time series of various parameters were reconstructed from encoded representations. This study shows the potential of applying machine learning techniques to extract physical insights from kinetic plasma simulations and sets the groundwork for future applications, including the construction of sub-grid models in fluid approaches.