Simulations and machine learning models for cosmic-ray short-term variations and test-mass charging on board LISA

Abstract

Energetic particles of galactic and solar origin charge the metal free-falling test masses (TMs) of the interferometers for gravitational wave detection in space. The deposited charge couples with stray electric fields thus generating spurious Coulomb forces between the TMs and the electrode housing that limit the interferometer sensitivity. Long-term and short-term galactic cosmic-ray variations are strongly energy-dependent and the TM charging varies with particle energy distribution. We propose three different approaches involving Monte Carlo simulations and machine learning models in comparison to particle transport with the Parker equation to study the recurrent modulation of energy spectra of galactic particles ascribable to the passage of high-speed solar wind streams. The transit of interplanetary counterparts of coronal mass ejections modifies the effects of high-speed streams. This work aims at better understanding the energy-dependence of galactic cosmic-ray short-term variations for the Laser Interferometer Space Antenna (LISA), the first interferometer for gravitational wave detection in space, starting from lessons learned with LISA Pathfinder. The outcomes of our models will be used to assess the TM charging during the time LISA will remain in orbit around the Sun.