Publications (47)
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…
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Xiangyang Ju, Steven Farrell, Paolo Calafiura +20
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and…
Sparse Data Generation for Particle-Based Simulation of Hadronic Jets in the LHC
Breno Orzari, Thiago Tomei, Maurizio Pierini +5
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset use…
Graph Neural Networks for Particle Tracking and Reconstruction
Javier Duarte, Jean-Roch Vlimant
Machine learning methods have a long history of applications in high energy physics (HEP). Recently, there is a growing interest in exploiting these methods to reconstruct particle…
Novel deep learning methods for track reconstruction
Steven Farrell, Paolo Calafiura, Mayur Mudigonda +11
For the past year, the HEP.TrkX project has been investigating machine learning solutions to LHC particle track reconstruction problems. A variety of models were studied that drew…
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
Cenk Tüysüz, Carla Rieger, Kristiane Novotny +6
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminos…
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 Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Raghav Kansal, Javier Duarte, Breno Orzari +5
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training…
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini +2
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoenc…
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
Steven Tsan, Raghav Kansal, Anthony Aportela +6
Autoencoders have useful applications in high energy physics in anomaly detection, particularly for jets - collimated showers of particles produced in collisions such as those at t…
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba +2
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics. In this paper, we present Set-VAE, a particle-based var…
Progress towards an improved particle flow algorithm at CMS with machine learning
Farouk Mokhtar, Joosep Pata, Javier Duarte +3
The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CER…
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
Joosep Pata, Javier Duarte, Jean-Roch Vlimant +2
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the cal…
Particle Cloud Generation with Message Passing Generative Adversarial Networks
Raghav Kansal, Javier Duarte, Hao Su +6
In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machi…
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…
The Tracking Machine Learning challenge : Throughput phase
Sabrina Amrouche, Laurent Basara, Paolo Calafiura +18
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants…
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
Eric A. Moreno, Jean-Roch Vlimant, Maria Spiropulu +2
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trai…
Explaining machine-learned particle-flow reconstruction
Farouk Mokhtar, Raghav Kansal, Daniel Diaz +4
The particle-flow (PF) algorithm is used in general-purpose particle detectors to reconstruct a comprehensive particle-level view of the collision by combining information from dif…
Machine Learning for Particle Flow Reconstruction at CMS
Joosep Pata, Javier Duarte, Farouk Mokhtar +5
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on ca…
The Tracking Machine Learning challenge : Accuracy phase
Sabrina Amrouche, Laurent Basara, Paolo Calafiura +24
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the 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…
Graph Neural Networks in Particle Physics
Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces. Graph neural networks are trainable functions which operate on graphs---set…
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
Cheng Chen, Olmo Cerri, Thong Q. Nguyen +2
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events pr…
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
Cenk Tüysüz, Federico Carminati, Bilge Demirköz +6
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experim…
Particle Track Reconstruction with Quantum Algorithms
Cenk Tüysüz, Federico Carminati, Bilge Demirköz +6
Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increa…
Performance of Particle Tracking Using a Quantum Graph Neural Network
Cenk Tüysüz, Kristiane Novotny, Carla Rieger +7
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminos…
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
Jesus Arjona Martinez, Olmo Cerri, Maurizio Pierini +2
At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usu…
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…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
JEDI-net: a jet identification algorithm based on interaction networks
Eric A. Moreno, Olmo Cerri, Javier M. Duarte +7
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced…
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
Dawit Belayneh, Federico Carminati, Amir Farbin +13
Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced i…
Data Science and Machine Learning in Education
Gabriele Benelli, Thomas Y. Chen, Javier Duarte +20
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sop…
Charged particle tracking with quantum annealing-inspired optimization
Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant +4
At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for analysis are expected to face challenges due to scaling wit…
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
Oliver Knapp, Guenther Dissertori, Olmo Cerri +3
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Ano…
Interaction networks for the identification of boosted decays
Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant +6
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinar…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description
Jesus Arjona Martinez, Thong Q Nguyen, Maurizio Pierini +2
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model…
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
Ryan Liu, Abhijith Gandrakota, Jennifer Ngadiuba +2
The challenging environment of real-time data processing systems at the Large Hadron Collider (LHC) strictly limits the computational complexity of algorithms that can be deployed.…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
A Roadmap for HEP Software and Computing R&D for the 2020s
Johannes Albrecht, Antonio Augusto Alves, Guilherme Amadio +307
Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facil…
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
Mary Touranakou, Nadezda Chernyavskaya, Javier Duarte +6
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta…
Quantum Machine Learning in High Energy Physics
Wen Guan, Gabriel Perdue, Arthur Pesah +4
Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early…
Topology classification with deep learning to improve real-time event selection at the LHC
Thong Q. Nguyen, Daniel Weitekamp, Dustin Anderson +5
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consi…
Quantum computing for data analysis in high energy physics
Andrea Delgado, Kathleen E. Hamilton, Prasanna Date +22
Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and o…
An MPI-Based Python Framework for Distributed Training with Keras
Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu
We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs. The framework is built on the popular Keras machine learning library…
Distributed Training and Optimization Of Neural Networks
Jean-Roch Vlimant, Junqi Yin
Deep learning models are yielding increasingly better performances thanks to multiple factors. To be successful, model may have large number of parameters or complex architectures…
Quantum adiabatic machine learning with zooming
Alexander Zlokapa, Alex Mott, Joshua Job +3
Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific applicat…