1 citations · 1 across the 5 of their papers we have counts for
10 papers
AI Acceleration of Muon Collider R&D: A Report from the AI4MuC Workshop
J. Scott Berg, David Bizzozero, Tulika Bose +27
A Muon Collider is one of the most attractive routes to multi-TeV lepton collisions, but it requires the accelerator and detector communities to solve a tightly coupled set of prob…
A Phase-Space Inclusive Figure of Merit Based in Optimal Transport for Validating Monte Carlo Reweightings
Rishabh Jain, Lauren Hay, Matt LeBlanc +1
Validating whether the underlying physics of a model has been retained after a full phase-space reweighting poses a unique challenge. Often, validation relies on comparing histogra…
Optimizing Cell-Based Negative Weight Mitigation with Optimal Transport
Lauren Hay, Rishabh Jain, Matt LeBlanc +1
As the accuracy of experimental results in high energy physics (HEP) increases, so does the demand for precision Monte Carlo (MC) simulation. Higher-accuracy event generation at ne…
Fast BIB simulation at a future Muon Collider with generative machine learning
Radha Mastandrea, Shiyu Peng, Benjamin Rosser +1
Beam-induced background (BIB) from muon decay products will be an overwhelming and unavoidable background at a future Muon Collider. In order to develop robust event reconstruction…
Learning the Geometry of Collider Events with Metric-Aware Deep Sets
Lauren Hay, Rishabh Jain, Matt LeBlanc +1
Optimal transport gives structured data a geometry, but exact evaluation is costly in large pairwise analyses that exploit relationships among distances. Learned surrogates are fas…
Optimal-Transport-Based Cell Resampling for Negative and Pathological Event Weights
Regan Doherty, Lauren Hay, Rishabh Jain +4
Negative and pathologically large Monte Carlo event weights strain the computing budgets of experiments at the Large Hadron Collider. Cell resampling algorithms locally redistribut…
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…
Massive tree-level splitting functions beyond kinematical limits
Stefan Höche, Matt LeBlanc, Jennifer Roloff +1
We present a compact form of the massive tree-level QCD splitting functions and discuss a decomposition of the results in terms of lower-order expressions, scalar dipole a…
The Pareto Frontier of Resilient Jet Tagging
Rikab Gambhir, Matt LeBlanc, Yuanchen Zhou
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting th…
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Andrew Ferguson, Marisa LaFleur, Lars Ruthotto +97
This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 w…