Solar Wind Speed Estimate with Machine Learning Ensemble Models for LISA. (arXiv:2302.06740v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Sabbatini_F/0/1/0/all/0/1">Federico Sabbatini</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Grimani_C/0/1/0/all/0/1">Catia Grimani</a>
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.
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.
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