EGU24-12885, updated on 09 Mar 2024
EGU General Assembly 2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Finding Hidden Conjunctions in the Solar Wind

Zoe Faes1, Laura Hayes2, Daniel Müller2, and Andrew Walsh1
Zoe Faes et al.
  • 1European Space Agency, ESAC, Villanueva de la Cañada, Madrid, Spain
  • 2European Space Agency, ESTEC, Noordwijk, the Netherlands

This study aims to identify sets of in-situ measurements of the solar wind which sample the same volume of plasma at different times and locations as it travels through the heliosphere using ensemble machine learning methods. Multiple observations of a single volume of plasma by different spacecraft - referred to here as conjunctions - are becoming more frequent in the current “golden age of heliophysics research” and are key to characterizing the expansion of the solar wind. Specifically, identifying these related observations will enable us to test the current understanding of solar wind acceleration from the corona to the inner heliosphere with a more comprehensive set of measurements than has been used in previous analyses.

Using in-situ measurements of the background solar wind from Solar Orbiter, Parker Solar Probe, STEREO-A, Wind and BepiColombo, we identify a set of criteria based on features of magnetic field, velocity, density and temperature timeseries of known conjunctions and search for other instances for which the criteria are satisfied, to find previously unknown conjunctions. We use an ensemble of models, including random forests and recurrent neural networks with long short-term memory trained on synthetic observations obtained from magnetohydrodynamic simulations, to identify candidate conjunctions solely from kinetic properties of the solar wind. Initial results show a previously unidentified set of conjunctions between the spacecraft considered in this study. While this analysis has thus far only been performed on observations obtained since 2021 (start of Solar Orbiter science operations), the methods used here can be applied to other datasets to increase the potential for scientific return of existing and future heliophysics missions.

The modular scientific software built over the course of this research includes methods for the retrieval, processing, visualisation, and analysis of observational and synthetic timeseries of solar wind properties. It also includes methods for feature engineering and integration with widely used machine learning libraries. The software is available as an open-source Python package to ensure results can be easily reproduced and to facilitate further investigation of coordinated in-situ data in heliophysics.

How to cite: Faes, Z., Hayes, L., Müller, D., and Walsh, A.: Finding Hidden Conjunctions in the Solar Wind, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12885,, 2024.

Comments on the supplementary material

AC: Author Comment | CC: Community Comment | Report abuse

supplementary materials version 1 – uploaded on 12 Apr 2024, no comments