A database of MMS bow shock crossings compiled using machine learning

TL;DR

This paper introduces a machine learning-based method to automatically identify MMS spacecraft shock crossings, creating a comprehensive database that enables statistical analysis of shock properties and ion acceleration efficiency.

Contribution

It presents a novel machine learning approach to automate shock crossing detection and compiles a detailed database for advanced shock physics research.

Findings

Shocks in the database are distributed across real and parameter space.

No clear correlation between ion acceleration efficiency and Mach number.

Quasi-parallel shocks are more efficient at accelerating ions.

Abstract

Identifying collisionless shock crossings in data sent from spacecraft has so far been done manually. It is a tedious job that shock physicists have to go through if they want to conduct case studies or perform statistical studies. We use a machine learning approach to automatically identify shock crossings from the Magnetospheric Multiscale (MMS) spacecraft. We compile a database of those crossings including various spacecraft related and shock related parameters for each event. Furthermore, we show that the shocks in the database have properties that are spread out both in real space and parameter space. We also present a possible science application of the database by looking for correlations between ion acceleration efficiency at shocks and different shock parameters such as $\theta_{Bn}$ and $M_A$. Furthermore, we investigate statistically the ion acceleration efficiency. We find no clear correlation between the acceleration efficiency and $M_A$ and we find that quasi-parallel shocks are more efficient at accelerating ions.