Prediction and understanding of soft proton contamination in XMM-Newton: a machine learning approach

One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes flying mainly in the magnetosphere are soft protons with few tens to hundreds of keV concentrated. One such telescope is the X-ray Multi-Mirror Mission (XMM-Newton) by ESA. Its observing time lost due to the contamination is  about 40%. This affects all the major broad science goals of XMM, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future X-ray missions such Athena and SMILE missions. Magnetopsheric processes that trigger this background are still poorly understood. We use a machine learning approach to delineate related important parameters and to develop a model to predict the background contamination using 12 years of XMM observations. As predictors we use the location of XMM, solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in southern direction, ZGSE, (XMM observations were in the southern hemisphere), the solar wind velocity and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the best two individual predictors and a machine learning model which utilizes an ensemble of the predictors (Extra Trees Regressor) and gives better performance. Based on our analysis, future X-Ray missions in the magnetosphere should minimize observations during  times  associated with high solar wind speed  and avoid closed magnetic field lines, especially at the dusk flank region at least in the southern hemisphere.