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Found 19 papers, 12 papers with code
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.
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
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.
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.
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
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.
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
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.
Processing Columnar Collider Data with GPU-Accelerated Kernels
At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task.
Data Analysis, Statistics and Probability Distributed, Parallel, and Cluster Computing Computational Physics
Fast Particle-based Anomaly Detection Algorithm with Variational Autoencoder
Model-agnostic anomaly detection is one of the promising approaches in the search for new beyond the standard model physics.
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.
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.
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.
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.
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
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
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
The recurrent autoencoder outperforms other autoencoders based on different architectures.
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
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.
