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Jet Tagging
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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.
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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fast Jet Tagging with MLP-Mixers on FPGAs
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
Interpreting Transformers for Jet Tagging
Lie-Equivariant Quantum Graph Neural Networks
Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
MACK: Mismodeling Addressed with Contrastive Knowledge
A multicategory jet image classification framework using deep neural network
Particle Multi-Axis Transformer for Jet Tagging
Gradient-based Automatic Mixed Precision Quantization for Neural Networks On-Chip
Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey
OmniJet-$α$: The first cross-task foundation model for particle physics
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
A Comparison Between Invariant and Equivariant Classical and Quantum Graph Neural Networks
SAIBench: A Structural Interpretation of AI for Science Through Benchmarks
JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables
Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation
19 Parameters Is All You Need: Tiny Neural Networks for Particle Physics
PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions
Graph Structure from Point Clouds: Geometric Attention is All You Need
Retrieval of Boost Invariant Symbolic Observables via Feature Importance
Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
Do graph neural networks learn traditional jet substructure?
Jet tagging algorithm of graph network with HaarPooling message passing
BIP: Boost Invariant Polynomials for Efficient Jet Tagging
Exploring the Universality of Hadronic Jet Classification
Improving Robustness of Jet Tagging Algorithms with Adversarial Training
Semi-Equivariant GNN Architectures for Jet Tagging
Particle Transformer for Jet Tagging
Invariant Representation Driven Neural Classifier for Anti-QCD Jet Tagging
Autoencoders for unsupervised anomaly detection in high energy physics
Point Cloud Transformers applied to Collider Physics
Jet tagging in the Lund plane with graph networks
Linearized Optimal Transport for Collider Events
Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.