Papers Jet Tagging
“Jet Tagging” 태그가 달린 논문 67편 · 필터 해제
Reconfigurable Computing Challenge: Transformer for Jet Tagging on Versal AI Engines
Transformer-based models achieve strong performance for jet tagging at the CERN LHC, but deploying them in low-latency, resource-constrained trigger systems is challenging. We present an initial implementation of a quant…
Jet TaggingJetParticle-JEPA: An Efficient Self-Supervised Representation Learning method for Jet Tagging in High-Energy Physics
Jet tagging at the Large Hadron Collider increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce…
Representation LearningJet TaggingPatch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
Real-time jet tagging is critical for identifying short-lived particle decays in the high-throughput detectors of the Large Hadron Collider, where real-time trigger systems responsible for deciding which collision events…
Jet TaggingDissecting Jet-Tagger Through Mechanistic Interpretability
Mechanistic interpretability seeks to reverse engineer a trained neural network by identifying the minimal subset of internal components. We perform a mechanistic interpretability analysis of the Particle Transformer arc…
Jet TaggingTransfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics
Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this …
Transfer LearningJet TaggingExplainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane
Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performance on jet tagging benchmarks at the Large Hadron Collider, yet the ph…
Point CloudsJet TaggingPQuantML: A Tool for End-to-End Hardware-aware Model Compression
PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict latency constraints, …
Model CompressionJet TaggingB-jet Tagging Using a Hybrid Edge Convolution and Transformer Architecture
Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring t…
Jet TaggingNeural Scaling Laws for Boosted Jet Tagging
The success of Large Language Models (LLMs) has established that scaling compute, through joint increases in model capacity and dataset size, is the primary driver of performance in modern machine learning. While machine…
Jet TaggingJetFormer: A Scalable and Efficient Transformer for Jet Tagging from Offline Analysis to FPGA Triggers
We present JetFormer, a versatile and scalable encoder-only Transformer architecture for particle jet tagging at the Large Hadron Collider (LHC). Unlike prior approaches that are often tailored to specific deployment reg…
Computational EfficiencyJet TaggingTowards Tensor Network Models for Low-Latency Jet Tagging on FPGAs
We present a systematic study of Tensor Network (TN) models $\unicode{x2013}$ Matrix Product States (MPS) and Tree Tensor Networks (TTN) $\unicode{x2013}$ for real-time jet tagging in high-energy physics, with a focus on…
Jet TaggingKIGNet: Physics-Motivated Multi-Graph Representation Learning for Explainable Jet Tagging
Jet identification plays a central role in analyzing data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability. We introduce the Kinematic Interactio…
Graph Representation LearningGraph Neural NetworkJet TaggingSURFing to the Fundamental Limit of Jet Tagging
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits? Generative surrogate models with learn…
Jet TaggingSEAL - A Symmetry EncourAging Loss for High Energy Physics
Physical symmetries provide a strong inductive bias for constructing functions to analyze data. In particular, this bias may improve robustness, data efficiency, and interpretability of machine learning models. However, …
Jet TaggingSub-microsecond Transformers for Jet Tagging on FPGAs
We present the first sub-microsecond transformer implementation on an FPGA achieving competitive performance for state-of-the-art high-energy physics benchmarks. Transformers have shown exceptional performance on multipl…
Jet TaggingSpatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
Transformers are very effective in capturing both global and local correlations within high-energy particle collisions, but they present deployment challenges in high-data-throughput environments, such as the CERN LHC. T…
Point Cloud ClassificationJet TaggingQINNs: Quantum-Informed Neural Networks
Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a gene…
Jet TaggingTrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs
The Transformer Machine Learning (ML) architecture has been gaining considerable momentum in recent years. In particular, computational High-Energy Physics tasks such as jet tagging and particle track reconstruction (tra…
Jet TaggingBigBang-Proton Technical Report: Next-Word-Prediction is Scientific Multitask Learner
We introduce BigBang-Proton, a unified sequence-based architecture for auto-regressive language modeling pretrained on cross-scale, cross-structure, cross-discipline real-world scientific tasks to construct a scientific …
Jet TaggingThe Pareto Frontier of Resilient Jet Tagging
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best performance using metrics such as…
Jet Tagging