Search Results for author:
Found 36 papers, 19 papers with code
Data Science and Machine Learning in Education
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 sophisticated analyses at the heart of HEP research.
Machine Learning for Particle Flow Reconstruction at CMS
The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy.
Particle-based Fast Jet Simulation at the LHC with Variational Autoencoders
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC.
Explaining machine-learned particle-flow reconstruction
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 different subdetectors.
Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance
We explore the use of graph-based autoencoders, which operate on jets in their "particle cloud" representations and can leverage the interdependencies among the particles within a jet, for such tasks.
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 power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
This increase in luminosity will significantly increase the number of particles interacting with the detector.
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
The recurrent autoencoder outperforms other autoencoders based on different architectures.
Particle Cloud Generation with Message Passing Generative Adversarial Networks
We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models.
The Tracking Machine Learning challenge : Throughput phase
Both were measured on the Codalab platform where the participants had to upload their software.
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
The Exa. TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC).
Object Reconstruction
High Energy Physics - Experiment
MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
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 calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum.
Distributed Training and Optimization Of Neural Networks
Deep learning models are yielding increasingly better performances thanks to multiple factors.
Graph Neural Networks for Particle Tracking and Reconstruction
In this chapter, we recapitulate the mathematical formalism of GNNs and highlight aspects to consider when designing these networks for HEP data, including graph construction, model architectures, learning objectives, and graph pooling.
graph construction
High Energy Physics - Phenomenology
High Energy Physics - Experiment
Data Analysis, Statistics and Probability
Performance of Particle Tracking Using a Quantum Graph Neural Network
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 (luminosity) and become the High Luminosity LHC.
Quantum Physics
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC).
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets.
Graph Neural Networks in Particle Physics
Particle physics is a branch of science aiming at discovering the fundamental laws of matter and forces.
High Energy Physics - Experiment High Energy Physics - Phenomenology
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
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) experiments.
Quantum Physics
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables.
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark
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.
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.
Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability
Particle Track Reconstruction with Quantum Algorithms
In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical closeness of the hits.
Quantum Physics High Energy Physics - Experiment Data Analysis, Statistics and Probability
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
Interaction networks for the identification of boosted $H\to b\overline{b}$ decays
We develop a jet identification algorithm based on an interaction network, designed to identify high-momentum Higgs bosons decaying to bottom quark-antiquark pairs, distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
JEDI-net: a jet identification algorithm based on interaction networks
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 at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons.
High Energy Physics - Experiment High Energy Physics - Phenomenology
Charged particle tracking with quantum annealing-inspired optimization
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 with track density.
Quantum adiabatic machine learning with zooming
The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a new class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events.
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup.
Novel deep learning methods for track reconstruction
The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.
High Energy Physics - Experiment Data Analysis, Statistics and Probability
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
Topology classification with deep learning to improve real-time event selection at the LHC
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.
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
An MPI-Based Python Framework for Distributed Training with Keras
We present a lightweight Python framework for distributed training of neural networks on multiple GPUs or CPUs.
