Using Machine Learning to Locate Three-Dimensional Magnetic Reconnection within PHASMA

The PHASMA (PHAse Space MApping) facility at WVU uses pulsed plasma guns to investigate magnetic reconnection through the interaction of two magnetic flux ropes. This study makes use of parameters such as the squashing factor and the Quasi-Separatrix Layer (QSL) to identify and locate magnetic reconnection within PHASMA. Based on these parameters, we attempt to predict where magnetic reconnection occurs at a distant location based on a predictive neural network that uses nonlocal (edge) magnetic measurements and line-integrated fast photodiode measurements in PHASMA. Initial validation of the machine learning algorithm was performed on magnetic field data from the Large Plasma Physics Device (LAPD) at the UCLA Basic Science Plasma Facility. This analysis will enable new studies of reconnection in highly turbulent and irreproducible systems by providing a means of localizing the time and location of reconnection to better synchronize triggered measurements, such as Thomson scattering measurements of the electron velocity distribution functions.