# Automatic detection of Interplanetary Coronal Mass Ejections from in-situ data: a deep learning approach

## TL;DR

This paper introduces a deep learning method using convolutional neural networks for automatic, fast, and multi-scale detection of Interplanetary Coronal Mass Ejections (ICMEs) in in-situ spacecraft data, reducing bias and increasing reproducibility.

## Contribution

The authors develop a convolutional neural network-based approach for automatic ICME detection, outperforming traditional visual methods and handling incomplete data effectively.

## Key findings

- Detected 84% of ICMEs in 2010-2015 period with high recall.
- Achieved 84% precision with minimal false positives.
- Method generalizes to different event signatures and missing data.

## Abstract

Decades of studies have suggested several criteria to detect Interplanetary coronal mass ejections (ICME) in time series from in-situ spacecraft measurements. Among them the most common are an enhanced and smoothly rotating magnetic field, a low proton temperature and a low plasma beta. However, these features are not all observed for each ICME due to their strong variability. Visual detection is time-consuming and biased by the observer interpretation leading to non exhaustive, subjective and thus hardly reproducible catalogs. Using convolutional neural networks on sliding windows and peak detection, we provide a fast, automatic and multi-scale detection of ICMEs. The method has been tested on the in-situ data from WIND between 1997 and 2015 and on the 657 ICMEs that were recorded during this period. The method offers an unambiguous visual proxy of ICMEs that gives an interpretation of the data similar to what an expert observer would give. We found at a maximum 197 of the 232 ICMEs of the 2010-2015 period (recall 84 +-4.5 % including 90% of the ICMEs present in the lists of Nieves-Chinchilla et al. (2015) and Chi et al. (2016). The minimal number of False Positives was 25 out of 158 predicted ICMEs (precision 84+-2.6%). Although less accurate, the method also works with one or several missing input parameters. The method has the advantage of improving its performance by just increasing the amount of input data. The generality of the method paves the way for automatic detection of many different event signatures in spacecraft in-situ measurements.