70 citations · 117 across the 11 of their papers we have counts for
12 papers
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
Zixin Ding, Shaghayegh Emami, Giovanna Salvi +7
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and…
End-to-end optimisation of HEP triggers
Noah Clarke Hall, Ioannis Xiotidis, Nikos Konstantinidis +1
High-energy physics experiments face extreme data rates, requiring real-time trigger systems to reduce event throughput while preserving sensitivity to rare processes. Trigger syst…
Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision
Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457
Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…
Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time
Shaghayegh Emami, Cecilia Tosciri, Giovanna Salvi +7
Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process ext…
Bridging the divide: axion searches and axino phenomenology at colliders
Gabe Hoshino, Kristin Dona, Keisuke Harigaya +4
We discuss a phenomenological model that extends the minimal supersymmetric standard model to contain axions and their supersymmetric partner, the axino. In the supersymmetric DFSZ…
Explainable Equivariant Neural Networks for Particle Physics: PELICAN
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +2
PELICAN is a novel permutation equivariant and Lorentz invariant or covariant aggregator network designed to overcome common limitations found in architectures applied to particle…
PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics
Alexander Bogatskiy, Timothy Hoffman, David W. Miller +1
Many current approaches to machine learning in particle physics use generic architectures that require large numbers of parameters and disregard underlying physics principles, limi…
Innovations in trigger and data acquisition systems for next-generation physics facilities
Rainer Bartoldus, Catrin Bernius, David W. Miller
Data-intensive physics facilities are increasingly reliant on heterogeneous and large-scale data processing and computational systems in order to collect, distribute, process, filt…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…
Quantized Gromov-Wasserstein
Samir Chowdhury, David Miller, Tom Needham
The Gromov-Wasserstein (GW) framework adapts ideas from optimal transport to allow for the comparison of probability distributions defined on different metric spaces. Scalable comp…
Towards an Interpretable Data-driven Trigger System for High-throughput Physics Facilities
Chinmaya Mahesh, Kristin Dona, David W. Miller +1
Data-intensive science is increasingly reliant on real-time processing capabilities and machine learning workflows, in order to filter and analyze the extreme volumes of data being…
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann +3
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The…