94 citations · 290 across the 23 of their papers we have counts for
28 papers
Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
Jonathan Renusch, Benjamin Huth, Daniel Murnane +9
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linea…
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi +5
Reconstruction and simulation at a collider experiment are long chains of specialised algorithms, each tuned to a single step. We explore how one model can serve many of those step…
The Landscape of Composite Higgs Models
Daniel Murnane
While the Standard Model (SM) of particle physics contains the most precise set of predictions ever devised by humanity, that precision comes at a cost. The strange nature of the H…
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…
ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset
Doğa Elitez, Paul Gessinger, Daniel Murnane +5
We introduce ColliderML - a large, open, experiment-agnostic dataset of fully simulated and digitised proton-proton collisions in High-Luminosity Large Hadron Collider conditions (…
Physics Briefing Book: Input for the 2026 update of the European Strategy for Particle Physics
Jorge de Blas, Monica Dunford, Emanuele Bagnaschi +127
The European Strategy for Particle Physics (ESPP) reflects the vision and presents concrete plans of the European particle physics community for advancing human knowledge in fundam…
Machine Learning Power Week 2023: Clustering in Hadronic Calorimeters
Muaz Al Halabi, Marcel Bajdel, Jeroen Peter Bormans +19
In both high-energy physics and industry applications, a crowd-sourced approach to difficult problems is becoming increasingly common. These innovative approaches are ideal for the…
A Language Model for Particle Tracking
Andris Huang, Yash Melkani, Paolo Calafiura +4
Particle tracking is crucial for almost all physics analysis programs at the Large Hadron Collider. Deep learning models are pervasively used in particle tracking related tasks. Ho…
Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks
Savannah Thais, Daniel Murnane
Incorporating inductive biases into ML models is an active area of ML research, especially when ML models are applied to data about the physical world. Equivariant Graph Neural Net…
Graph Structure from Point Clouds: Geometric Attention is All You Need
Daniel Murnane
The use of graph neural networks has produced significant advances in point cloud problems, such as those found in high energy physics. The question of how to produce a graph struc…
Equivariant Graph Neural Networks for Charged Particle Tracking
Daniel Murnane, Savannah Thais, Ameya Thete
Graph neural networks (GNNs) have gained traction in high-energy physics (HEP) for their potential to improve accuracy and scalability. However, their resource-intensive nature and…
Hierarchical Graph Neural Networks for Particle Track Reconstruction
Ryan Liu, Paolo Calafiura, Steven Farrell +3
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which corr…
Heterogeneous Graph Neural Network for Identifying Hadronically Decayed Tau Leptons at the High Luminosity LHC
Andris Huang, Xiangyang Ju, Jacob Lyons +3
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algo…
Benchmarking GPU and TPU Performance with Graph Neural Networks
xiangyang Ju, Yunsong Wang, Daniel Murnane +3
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing U…
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
Savannah Thais, Paolo Calafiura, Grigorios Chachamis +7
Many physical systems can be best understood as sets of discrete data with associated relationships. Where previously these sets of data have been formulated as series or image dat…
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Chun-Yi Wang, Xiangyang Ju, Shih-Chieh Hsu +22
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalm…
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…
Semi-Equivariant GNN Architectures for Jet Tagging
Daniel Murnane, Savannah Thais, Jason Wong
Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable paramet…
Accelerating the Inference of the Exa.TrkX Pipeline
Alina Lazar, Xiangyang Ju, Daniel Murnane +21
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.Tr…
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
V Hewes, Adam Aurisano, Giuseppe Cerati +14
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still…
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
Xiangyang Ju, Daniel Murnane, Paolo Calafiura +21
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detecto…
Convergent Bayesian Global Fits of 4D Composite Higgs Models
Ethan Carragher, Will Handley, Daniel Murnane +4
Models in which the Higgs boson is a composite pseudo-Nambu-Goldstone boson offer attractive solutions to the Higgs mass naturalness problem. We consider three such models based on…
Minimal 4D Composite Higgs Models Under Current LHC Constraints
Ethan Carragher, Daniel Murnane, Peter Stangl +3
We present preliminary results of the first convergent global fits of several minimal composite Higgs models. Our fits are performed using the differential evolution optimisation p…
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Nicholas Choma, Daniel Murnane, Xiangyang Ju +16
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promisi…