Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …

In this community review report, we discuss applications and techniques for fast machine
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …

The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation
is determined, based on a full simulation, reconstruction, and event selection of the far …

This paper presents a quantum convolutional neural network (QCNN) for the classification of
high energy physics events. The proposed model is tested using a simulated dataset from …

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long-baseline
neutrino oscillation experiment consisting of a high-power, broadband neutrino beam, a …

The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino
oscillation measurements over the lifetime of the experiment. In this work, we explore …

The high energy physics (HEP) community has a long history of dealing with large-scale
datasets. To manage such voluminous data, classical machine learning and deep learning …

Monte Carlo simulation of particle tracking in matter is the reference simulation method in
the field of medical physics. It is heavily used in various applications such as 1) patient dose …

Machine learning algorithms are becoming increasingly prevalent and performant in the
reconstruction of events in accelerator-based neutrino experiments. These sophisticated …