Prediction of Soft Proton Contamination in XMM-Newton: a Machine Learning Approach

Abstract:

One of the major sources of background for the current generation of X-ray telescopes are few tens to hundreds of keV (soft) protons concentrated by the mirrors. One such telescope is the ESA's X-ray Multi-Mirror Mission (XMM-Newton). Its observing time lost due to background contamination is about 40%. The loss of observing time affects all the major broad science goals of this observatory, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future large X-ray missions such as the ESA's planned Athena mission. Physical processes that trigger this background contamination are still poorly understood. We use a machine learning approach to delineate related important space weather parameters and to develop a model to predict the background contamination using 12 years of XMM-Newton observations. We revealed that the contamination is most strongly related to the distance in southern direction, (XMM-Newton observations were in the southern hemisphere), the solar wind radial velocity and the location on the magnetospheric magnetic field lines. Based on our analysis, future missions should minimize observations during times associated with high solar wind speed, avoid closed magnetic field lines, and the dusk flank regions.