Machine learning in the search for new fundamental physics
Compelling experimental evidence suggests the existence of new physics beyond the well-
established and tested standard model of particle physics. Various current and upcoming …
established and tested standard model of particle physics. Various current and upcoming …
[HTML][HTML] Applications and techniques for fast machine learning in science
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 …
learning (ML) in science—the concept of integrating powerful ML methods into the real-time …
Long-baseline neutrino oscillation physics potential of the DUNE experiment
B Abi, R Acciarri, MA Acero, G Adamov… - The European Physical …, 2020 - Springer
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 …
is determined, based on a full simulation, reconstruction, and event selection of the far …
Quantum convolutional neural networks for high energy physics data analysis
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 …
high energy physics events. The proposed model is tested using a simulated dataset from …
Hybrid quantum-classical graph convolutional network
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 …
datasets. To manage such voluminous data, classical machine learning and deep learning …
Low exposure long-baseline neutrino oscillation sensitivity of the DUNE experiment
AA Abud, B Abi, R Acciarri, MA Acero, MR Adames… - Physical Review D, 2022 - APS
The Deep Underground Neutrino Experiment (DUNE) will produce world-leading neutrino
oscillation measurements over the lifetime of the experiment. In this work, we explore …
oscillation measurements over the lifetime of the experiment. In this work, we explore …
Artificial intelligence for Monte Carlo simulation in medical physics
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 …
the field of medical physics. It is heavily used in various applications such as 1) patient dose …
A review on machine learning for neutrino experiments
F Psihas, M Groh, C Tunnell… - International Journal of …, 2020 - World Scientific
Neutrino experiments study the least understood of the Standard Model particles by
observing their direct interactions with matter or searching for ultra-rare signals. The study of …
observing their direct interactions with matter or searching for ultra-rare signals. The study of …
GPU-accelerated machine learning inference as a service for computing in neutrino experiments
Machine learning algorithms are becoming increasingly prevalent and performant in the
reconstruction of events in accelerator-based neutrino experiments. These sophisticated …
reconstruction of events in accelerator-based neutrino experiments. These sophisticated …
Graph neural network for neutrino physics event reconstruction
A Aurisano, V Hewes, G Cerati, J Kowalkowski, CS Lee… - Physical Review D, 2024 - APS
Liquid argon time projection chamber (LArTPC) detector technology offers a wealth of high-
resolution information on particle interactions, and leveraging that information to its full …
resolution information on particle interactions, and leveraging that information to its full …