<ul><li>Liquid argon time projection chambers are often used in neutrino physics and dark-matter searches because of their high spatial resolution.</li><li>Traditional machine learning methods such as convolutional neural networks (CNNs) cannot operate directly on the sparse matrix representation of the detector data.</li><li>A machine learning model using a point set neural network is proposed, which greatly improves processing speed and accuracy over methods that instantiate the dense matrix.</li><li>Compared to competing methods, the proposed model improves classification and segmentation performance while significantly reducing time and memory requirements.</li></ul>

Particle Hit Clustering and Identification Using Point Set Transformers in Liquid Argon Time Projection Chambers

Discover more