# Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA

arXiv: https://arxiv.org/abs/2302.06740 · HTML: https://mlsanity.com/paper/2302.06740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 50 references that are in the map.
- First posted on arXiv 13 Feb 2023.

## Builds on (most cited first, 3 of 3)

- [A Spatial Agent-Based Model for Preemptive Evacuation Decisions During Typhoon](https://mlsanity.com/paper/2109.14459/) (2021)
- [Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data](https://mlsanity.com/paper/2205.03578/) (2022)
- [Symbolic Knowledge Extraction from Opaque Predictors Applied to Cosmic-Ray Data Gathered with LISA Pathfinder](https://mlsanity.com/paper/2209.04697/) (2022)

## Abstract (arXiv)

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.