Gautier Nguyen | Searcharxiv

arXiv2026

Towards a Fully Automated Pipeline for Short-Term Forecasting of In Situ Coronal Mass Ejection Magnetic Field Structure

We present an automated pipeline for operational short-term forecasting of coronal mass ejection (CME) magnetic field structure at L1, coupling arrival time prediction, in situ detection, and iterative flux rope reconstruction, following near-real-time remote-sensing CME identification. The system is triggered by new entries in the CCMC DONKI database and first applies the drag-based ELEvo model to determine whether an Earth impact is expected and estimate arrival time. This estimate defines a temporal window constraining the search for CME signatures in real-time L1 in situ solar wind data, where the magnetic obstacle (MO) is automatically detected using the deep learning model ARCANE. Upon MO onset, iterative reconstructions with the semi-empirical flux rope model 3DCORE are performed, using a Monte Carlo fitting scheme, producing continuously updated forecasts of the remaining magnetic field profile. We evaluate the pipeline using 3870 archived DONKI entries and archived NOAA real-time in situ data from 2013-2025, assessing forecast performance at different stages of MO observation. For 61 events with an associated ground-truth counterpart in the ICMECAT catalog, forecasts based on initial MO data already achieve performance comparable to full-event reconstructions. Typical errors are ~5 hours in timing of magnetic field extrema and ~10 nT in field strength metrics, with limited systematic improvement as more of the event is observed. Results show substantial event variability and systematic underestimation of extrema, indicating deviations from ideal flux rope assumptions. These findings demonstrate the potential of fully autonomous real-time forecasting while highlighting limitations imposed by event complexity and model representational capacity.

astro-ph.SR↗

arXiv2019

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

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.

astro-ph.SR↗