Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA
This work addresses the problem of space weather diagnostics for missions like LISA by offering a software-based surrogate, though it appears incremental in applying existing ensemble methods to new data.
The study tackled reconstructing solar wind speed from cosmic-ray flux data using machine learning ensemble models, achieving improved predictive accuracy compared to individual weak regressors.
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.