Pretrained Event Classification Model for High Energy Physics Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | Ho, Joshua, Roberts, Benjamin Ryan, Han, Shuo, Wang, Haichen |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Versatile Energy-Based Probabilistic Models for High Energy Physics
by: Cheng, Taoli, et al.
Published: (2023)
by: Cheng, Taoli, et al.
Published: (2023)
SEAL - A Symmetry EncourAging Loss for High Energy Physics
by: Hebbar, Pradyun, et al.
Published: (2025)
by: Hebbar, Pradyun, et al.
Published: (2025)
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
by: Leigh, Matthew, et al.
Published: (2023)
by: Leigh, Matthew, et al.
Published: (2023)
Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
by: Qiu, Shikai, et al.
Published: (2023)
by: Qiu, Shikai, et al.
Published: (2023)
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
by: Unlu, Eyup B., et al.
Published: (2024)
by: Unlu, Eyup B., et al.
Published: (2024)
Quantum Attention for Vision Transformers in High Energy Physics
by: Tesi, Alessandro, et al.
Published: (2024)
by: Tesi, Alessandro, et al.
Published: (2024)
Improving Neutrino Oscillation Measurements through Event Classification
by: Ellis, Sebastian A. R., et al.
Published: (2025)
by: Ellis, Sebastian A. R., et al.
Published: (2025)
EveNet: A Foundation Model for Particle Collision Data Analysis
by: Hsu, Ting-Hsiang, et al.
Published: (2026)
by: Hsu, Ting-Hsiang, et al.
Published: (2026)
Event Generators for High-Energy Physics Experiments
by: Campbell, J. M., et al.
Published: (2022)
by: Campbell, J. M., et al.
Published: (2022)
Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
by: Vatellis, Vasileios
Published: (2024)
by: Vatellis, Vasileios
Published: (2024)
Feature Learning and Generalization in Deep Networks with Orthogonal Weights
by: Day, Hannah, et al.
Published: (2023)
by: Day, Hannah, et al.
Published: (2023)
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
by: Golling, Tobias, et al.
Published: (2024)
by: Golling, Tobias, et al.
Published: (2024)
Automating High Energy Physics Data Analysis with LLM-Powered Agents
by: Gendreau-Distler, Eli, et al.
Published: (2025)
by: Gendreau-Distler, Eli, et al.
Published: (2025)
Bumblebee: Foundation Model for Particle Physics Discovery
by: Wildridge, Andrew J., et al.
Published: (2024)
by: Wildridge, Andrew J., et al.
Published: (2024)
Comparing Generative Models with the New Physics Learning Machine
by: Grossi, Samuele, et al.
Published: (2025)
by: Grossi, Samuele, et al.
Published: (2025)
Data-Driven High-Dimensional Statistical Inference with Generative Models
by: Amram, Oz, et al.
Published: (2025)
by: Amram, Oz, et al.
Published: (2025)
Improving Generative Model-based Unfolding with Schrödinger Bridges
by: Diefenbacher, Sascha, et al.
Published: (2023)
by: Diefenbacher, Sascha, et al.
Published: (2023)
High-dimensional and Permutation Invariant Anomaly Detection
by: Mikuni, Vinicius, et al.
Published: (2023)
by: Mikuni, Vinicius, et al.
Published: (2023)
Discriminative versus Generative Approaches to Simulation-based Inference
by: Sluijter, Benjamin, et al.
Published: (2025)
by: Sluijter, Benjamin, et al.
Published: (2025)
Aspen Open Jets: Unlocking LHC Data for Foundation Models in Particle Physics
by: Amram, Oz, et al.
Published: (2024)
by: Amram, Oz, et al.
Published: (2024)
Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
by: Mastandrea, Radha, et al.
Published: (2024)
by: Mastandrea, Radha, et al.
Published: (2024)
From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs
by: Bal, Aritra, et al.
Published: (2026)
by: Bal, Aritra, et al.
Published: (2026)
TASI Lectures on Physics for Machine Learning
by: Halverson, Jim
Published: (2024)
by: Halverson, Jim
Published: (2024)
Integrating Physics Inspired Features with Graph Convolution
by: Sahu, Rameswar
Published: (2024)
by: Sahu, Rameswar
Published: (2024)
Descending into the Modular Bootstrap
by: Benjamin, Nathan, et al.
Published: (2026)
by: Benjamin, Nathan, et al.
Published: (2026)
A Perspective on Symbolic Machine Learning in Physical Sciences
by: Makke, Nour, et al.
Published: (2025)
by: Makke, Nour, et al.
Published: (2025)
Explainable Equivariant Neural Networks for Particle Physics: PELICAN
by: Bogatskiy, Alexander, et al.
Published: (2023)
by: Bogatskiy, Alexander, et al.
Published: (2023)
Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
by: Spinner, Jonas, et al.
Published: (2024)
by: Spinner, Jonas, et al.
Published: (2024)
A comparison of Bayesian sampling algorithms for high-dimensional particle physics and cosmology applications
by: Albert, Joshua, et al.
Published: (2024)
by: Albert, Joshua, et al.
Published: (2024)
Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction
by: Faroughy, Darius A., et al.
Published: (2026)
by: Faroughy, Darius A., et al.
Published: (2026)
Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
by: Konar, Partha, et al.
Published: (2025)
by: Konar, Partha, et al.
Published: (2025)
Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
by: Arganda, Ernesto, et al.
Published: (2024)
by: Arganda, Ernesto, et al.
Published: (2024)
Unraveling particle dark matter with Physics-Informed Neural Networks
by: Bento, M. P., et al.
Published: (2025)
by: Bento, M. P., et al.
Published: (2025)
Physics-informed neural networks viewpoint for solving the Dyson-Schwinger equations of quantum electrodynamics
by: Terin, Rodrigo Carmo
Published: (2024)
by: Terin, Rodrigo Carmo
Published: (2024)
Training 3D ResNets to Extract BSM Physics Parameters from Simulated Data
by: Dubey, S., et al.
Published: (2023)
by: Dubey, S., et al.
Published: (2023)
Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
by: Krzmanc, Gregor, et al.
Published: (2026)
by: Krzmanc, Gregor, et al.
Published: (2026)
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
by: Shmakov, Alexander, et al.
Published: (2024)
by: Shmakov, Alexander, et al.
Published: (2024)
Generative Unfolding with Distribution Mapping
by: Butter, Anja, et al.
Published: (2024)
by: Butter, Anja, et al.
Published: (2024)
Solving stiff dark matter equations via Jacobian Normalization with Physics-Informed Neural Networks
by: Bento, M. P., et al.
Published: (2026)
by: Bento, M. P., et al.
Published: (2026)
Generator Based Inference (GBI)
by: Cheng, Chi Lung, et al.
Published: (2025)
by: Cheng, Chi Lung, et al.
Published: (2025)
Similar Items
-
Versatile Energy-Based Probabilistic Models for High Energy Physics
by: Cheng, Taoli, et al.
Published: (2023) -
SEAL - A Symmetry EncourAging Loss for High Energy Physics
by: Hebbar, Pradyun, et al.
Published: (2025) -
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
by: Leigh, Matthew, et al.
Published: (2023) -
Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
by: Qiu, Shikai, et al.
Published: (2023) -
Hybrid Quantum Vision Transformers for Event Classification in High Energy Physics
by: Unlu, Eyup B., et al.
Published: (2024)