Background Suppression with Machine Learning in Volcano Muography

Abstract

In this work, a machine learning algorithm, specifically a deep neural network, is introduced to mitigate
background interference in muography applications, predominantly aimed at volcano imaging. The discussed
detector system is engineered to filter out the low-energy background by incorporating up to five
lead absorber layers interspersed among eight detectors. This detector system underwent a Monte-Carlo
(Geant4) simulation to create training samples for the machine learning algorithm. It demonstrated that the
devised deep neural network outperforms the traditional tracking algorithm in suppressing low-energy
background, thereby rendering significant enhancement via machine learning supplementation.