Background suppression for volcano muography with machine learning

Abstract

Abstract A machine learning 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 teaching sample for the machine learning 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 machine learning yields in a significant improvement. The target areas of volcanoes lie beneath approximately a kilometer of rock that only fraction of a percent of muons have enough energy to penetrate. The machine learning 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 brehmstrahlung, while the machine learning algorithm easily adapts to accept such patterns.