Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data

Rüdisser, Hannah T.; Windisch, Andreas; Amerstorfer, Ute; Möstl, Christian; Amerstorfer, Tanja; Bailey, Roger C.; Reiss, Martin A.

doi:10.1029/2022sw003149

Cited by 5 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, involving plasma information in MO identification can potentially improve previously established solar wind auto-identification models that focus only on magnetic parameters and rotations of the magnetic field. The machine learning classification studies by Nguyen et al (2019) and Rüdisser et al (2022) utilize both magnetic field and plasma parameters. However, those studies are window-based requiring a multidimensional time series as input.…”

Section: Introductionmentioning

confidence: 99%

Classifying different types of solar wind plasma with uncertainty estimations using machine learning

Narock,

Pal,

Arsham

et al. 2024

Preprint

View full text Add to dashboard Cite

Decades of in-situ solar wind measurements have clearly established variations in solar wind physical parameters. These variable parameters have been used to classify the solar wind magnetized plasma into different types with several classification schemes being developed using these parameters. These classification schemes, while useful for understanding the solar wind’s originating processes at the Sun and early detection of space weather events, have left open questions regarding which physical parameters are most useful for classification and how recent advances in our understanding of solar wind transients impact classification. In this work, we use neural networks trained with different solar wind magneticand plasma characteristics to automatically classify the solar wind in coronal hole, streamer belt, sector reversal and solar transients such as coronal mass ejections comprised of both magnetic obstacles and sheaths. Furthermore, our work demonstrates how probabilistic neural networks can enhance classification by including a measure of prediction uncertainty. Our work also provides a ranking of the parameters that lead to an improved classification scheme with∼ 96% accuracy. Our new scheme paves the way for incorporating uncertainty estimates into space weather forecasting with the potential to be implemented on real-time solar wind data.

show abstract

Section: Introductionmentioning

confidence: 99%

Classifying different types of solar wind plasma with uncertainty estimations using machine learning

Narock,

Pal,

Arsham

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent years have seen an exponential increase in the application of machine learning (ML) techniques to several fields, including physics and astrophysics (Nguyen et al, 2019;Zhou et al, 2021;Reiss et al, 2021;Rüdisser et al, 2022). ML-based tools provide predictions more accurate than other models due to their generalisation properties.…”

Section: Introductionmentioning

confidence: 99%

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

Sabbatini¹,

Grimani²

2023

Preprint

View full text Add to dashboard Cite

In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016-2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted onboard the LISA Pathfinder mission also orbiting around L1 during the same years. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as LISA and space weather science.

show abstract

“…Recent years have seen an exponential increase in the application of machine learning (ML) techniques to several fields, including physics and astrophysics Nguyen et al (2019); Reiss et al (2021); Rüdisser et al (2022); Zhou et al (2021). ML-based tools provide predictions more accurate than other models due to their generalisation properties.…”

Section: Introductionmentioning

confidence: 99%

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

Sabbatini,

Grimani

2023

Preprint

View full text Add to dashboard Cite

In this work we study the potentialities of machine learning models in reconstructing the solar wind speed observations gathered in the first Lagrangian point by the ACE satellite in 2016--2017 using as input data galactic cosmic-ray flux variations measured with particle detectors hosted on board the LISA Pathfinder mission also orbiting around L1 during the same years. We show that ensemble models composed of heterogeneous weak regressors are able to outperform weak regressors in terms of predictive accuracy. Machine learning and other powerful predictive algorithms open a window on the possibility of substituting dedicated instrumentation with software models acting as surrogates for diagnostics of space missions such as the European Space Agency LISA mission and space weather science.

show abstract

Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data

Cited by 5 publications

References 38 publications

Classifying different types of solar wind plasma with uncertainty estimations using machine learning

Classifying different types of solar wind plasma with uncertainty estimations using machine learning

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

Contact Info

Product

Resources

About