Jet Tagging
15 papers with code • 1 benchmarks • 1 datasets
Jet tagging is the process of identifying the type of elementary particle that initiates a "jet", i.e., a collimated spray of outgoing particles. It is essentially a classification task that aims to distinguish jets arising from particles of interest, such as the Higgs boson or the top quark, from other less interesting types of jets.
Libraries
Use these libraries to find Jet Tagging models and implementations
JetClass
Most implemented papers
ParticleNet: Jet Tagging via Particle Clouds
How to represent a jet is at the core of machine learning on jet physics.
Jet-Images -- Deep Learning Edition
Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons.
Variational Autoencoders for Anomalous Jet Tagging
To build a performant mass-decorrelated anomalous jet tagger, we propose the Outlier Exposed VAE (OE-VAE), for which some outlier samples are introduced in the training process to guide the learned information.
Jet tagging in the Lund plane with graph networks
fdreyer/LundNet
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The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider.
Point Cloud Transformers applied to Collider Physics
Methods for processing point cloud information have seen a great success in collider physics applications.
Particle Transformer for Jet Tagging
jet-universe/particle_transformer
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Based on the large dataset, we propose a new Transformer-based architecture for jet tagging, called Particle Transformer (ParT).
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
AnnikaStein/Adversarial-Training-for-Jet-Tagging
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We investigate the classifier response to input data with injected mismodelings and probe the vulnerability of flavor tagging algorithms via application of adversarial attacks.
BIP: Boost Invariant Polynomials for Efficient Jet Tagging
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP).
Do graph neural networks learn traditional jet substructure?
farakiko/xai4hep
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At the CERN LHC, the task of jet tagging, whose goal is to infer the origin of a jet given a set of final-state particles, is dominated by machine learning methods.
Graph Structure from Point Clouds: Geometric Attention is All You Need
murnanedaniel/geometricattention
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The use of graph neural networks has produced significant advances in point cloud problems, such as those found in high energy physics.
