Imagen de Portada

Machine Learning in High Energy Physics: A review of heavy-flavor jet tagging at the LHC

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Mondal, Spandan, Mastrolorenzo, Luca
Formato:	Preprint
Publicado:	2024
Materias:	High Energy Physics - Experiment Data Analysis, Statistics and Probability
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Run 3 performance and advances in heavy-flavor jet tagging in CMS
por: Sarkar, Uttiya
Publicado: (2024)

Jet flavor tagging with Particle Transformer for Higgs factories
por: Suehara, Taikan, et al.
Publicado: (2026)

Harnessing Hardware Acceleration in High-Energy Physics through High-Level Synthesis Techniques
por: López, Pelayo Leguina, et al.
Publicado: (2025)

Optimization-based Unfolding in High-Energy Physics
por: Gasperini, Simone, et al.
Publicado: (2026)

On the Statistical Interpretation of Discoveries in LHC Data
por: Chekanov, S. V., et al.
Publicado: (2026)

Log Gaussian Cox Process Background Modeling in High Energy Physics
por: Frid, Yuval, et al.
Publicado: (2025)

The High W Challenge: Robust Neutrino Energy Estimators for LArTPCs
por: Thorpe, Christopher, et al.
Publicado: (2025)

Graph theory inspired anomaly detection at the LHC
por: Araz, Jack Y., et al.
Publicado: (2025)

Improving robustness of jet tagging algorithms with adversarial training: exploring the loss surface
por: Stein, Annika
Publicado: (2023)

Machine-Learning-Based Method for Goodness-of-Fit Test in Amplitude Analysis
por: Hou, Huoyi, et al.
Publicado: (2025)

Towards Universal Unfolding of Detector Effects in High-Energy Physics using Denoising Diffusion Probabilistic Models
por: Pazos, Camila, et al.
Publicado: (2024)

Exploring the BSM parameter space with Neural Network aided Simulation-Based Inference
por: Chatterjee, Atrideb, et al.
Publicado: (2025)

Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
por: Boggia, Laura, et al.
Publicado: (2025)

High Level Reconstruction with Deep Learning using ILD Full Simulation
por: Suehara, Taikan, et al.
Publicado: (2024)

Development of systematic uncertainty-aware neural network trainings for binned-likelihood analyses at the LHC
por: CMS Collaboration
Publicado: (2025)

Does Lorentz-symmetric design boost network performance in jet physics?
por: Li, Congqiao, et al.
Publicado: (2022)

Neural network biased corrections: Cautionary study in background corrections for quenched jets
por: Stewart, David, et al.
Publicado: (2024)

TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics
por: Melkani, Yash, et al.
Publicado: (2024)

Nanosecond hardware regression trees in FPGA at the LHC
por: Serhiayenka, Pavel, et al.
Publicado: (2024)

The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
por: Golling, Tobias, et al.
Publicado: (2023)

Construction of the Global $χ^2$ Function for the Simultaneous Fitting of Correlated Energy-Dependent Cross Sections
por: Shao, Linquan, et al.
Publicado: (2026)

Event-by-event Comparison between Machine-Learning- and Transfer-Matrix-based Unfolding Methods
por: Backes, Mathias, et al.
Publicado: (2023)

Machine Learning-Assisted Unfolding for Neutrino Cross-section Measurements with the OmniFold Technique
por: Huang, Roger G., et al.
Publicado: (2025)

A lightweight analysis farm for fundamental physics experiments
por: Brommer, Sebastian, et al.
Publicado: (2025)

Large-Scale Pretraining and Finetuning for Efficient Jet Classification in Particle Physics
por: Zhao, Zihan, et al.
Publicado: (2024)

Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
por: Go, Yeonju, et al.
Publicado: (2024)

Learning Selection Cuts With Gradients
por: Hance, Mike, et al.
Publicado: (2025)

Statistical Issues on the Neutrino Mass Hierarchy with $Δχ^{2}$
por: Sawy, Fatma, et al.
Publicado: (2023)

Fast Posterior Probability Sampling with Normalizing Flows and Its Applicability in Bayesian analysis in Particle Physics
por: Baz, Mathias El, et al.
Publicado: (2023)

Uncertainty Propagation within Chained Models for Machine Learning Reconstruction of Neutrino-LAr Interactions
por: Douglas, Daniel, et al.
Publicado: (2024)

Machine Learning
por: Duarte, Javier M., et al.
Publicado: (2025)

Machine Learning for Anomaly Detection in Particle Physics
por: Belis, Vasilis, et al.
Publicado: (2023)

Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
por: Golling, Tobias, et al.
Publicado: (2024)

Learning the Standard Model Manifold: Bayesian Latent Diffusion for Collider Anomaly Detection
por: Patel, Jigar, et al.
Publicado: (2026)

New Deep Learning Data Analysis Method for PROSPECT using GAPE: Genetic Algorithm Powered Evolution
por: Adriamirado, M., et al.
Publicado: (2026)

Comparison of Machine Learning Approaches for Classifying Spinodal Events
por: Malviya, Ashwini, et al.
Publicado: (2024)

A First Full Physics Benchmark for Highly Granular Calorimeter Surrogates
por: Buss, Thorsten, et al.
Publicado: (2025)

Variational Autoencoders for At-Source Data Reduction and Anomaly Detection in High Energy Particle Detectors
por: Yue, Alexander, et al.
Publicado: (2024)

Sensitivity to New Physics Phenomena in Anomaly Detection: A Study of Untunable Hyperparameters
por: de Souza, Fernando Abreu, et al.
Publicado: (2025)

A simple tool for weighted averaging of inconsistent data sets
por: Trassinelli, Martino, et al.
Publicado: (2024)