Background suppression for volcano muography with machine learning

Abstract

Abstract A machine learning (ML) algorithm (deep neural network) is presented to suppress background in muography applications mainly targeting volcanoes. Additionally it could be applied for large scale geological structures, such as ophiolites. The detector system investigated in this article is designed to suppress the low energy background by applying up to 5 lead absorber layers arranged among 8 detectors. This complicated system was simulated with a Monte-Carlo based particle simulation to provide training sample for the ML algorithm. It is shown that the developed deep neural network is capable of suppressing the low energy background considerably better than the classical tracking algorithm, therefore this additional suppression with ML yields in a significant improvement. The target areas of volcanoes lie beneath approximately a kilometer of rock that only a fraction of a percent of muons have enough energy to penetrate. The ML algorithm takes advantage of the directional changes in the absorbers, as well as the correlation between the muons energy and the deposited energy in the detectors. Identifying very high energy muons is also a challenge: the classical algorithm discards considerable fraction of 1 TeV muons which create multiple hits due to bremsstrahlung, while the ML algorithm easily adapts to accept such patterns.