:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Sanz, Veronica
Format:	Preprint
Published:	2025
Subjects:	Machine Learning High Energy Physics - Phenomenology
Online Access:	https://arxiv.org/abs/2504.05174
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Artificial Intelligence and Symmetries: Learning, Encoding, and Discovering Structure in Physical Data
by: Sanz, Veronica
Published: (2026)

Strengthening Anomaly Awareness
by: Banda, Adam, et al.
Published: (2025)

Probing the flavour-blind SMEFT: EFT validity and the interplay of energy scales
by: Mantani, Luca, et al.
Published: (2025)

Going beyond Top EFT
by: Lessa, André, et al.
Published: (2023)

Learning the Simplicity of Scattering Amplitudes
by: Cheung, Clifford, et al.
Published: (2024)

The Landscape of Unfolding with Machine Learning
by: Huetsch, Nathan, et al.
Published: (2024)

Learning Broken Symmetries with Approximate Invariance
by: Nabat, Seth, et al.
Published: (2024)

TASI Lectures on Physics for Machine Learning
by: Halverson, Jim
Published: (2024)

Learning to Unscramble Feynman Loop Integrals with SAILIR
by: Shih, David
Published: (2026)

A Perspective on Symbolic Machine Learning in Physical Sciences
by: Makke, Nour, et al.
Published: (2025)

Comparing Generative Models with the New Physics Learning Machine
by: Grossi, Samuele, et al.
Published: (2025)

Feature Learning and Generalization in Deep Networks with Orthogonal Weights
by: Day, Hannah, et al.
Published: (2023)

Learning to Validate Generative Models: a Goodness-of-Fit Approach
by: Cappelli, Pietro, et al.
Published: (2025)

Refining Integration-by-Parts Reduction of Feynman Integrals with Machine Learning
by: von Hippel, Matt, et al.
Published: (2025)

Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC
by: Arganda, Ernesto, et al.
Published: (2024)

Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
by: Harris, Philip, et al.
Published: (2024)

Data-driven discovery strategy for standard model effective field theory searches
by: Hirsch, Martin, et al.
Published: (2025)

Learning the S-matrix from data: Rediscovering gravity from gauge theory via symbolic regression
by: Moynihan, Nathan
Published: (2026)

PCN: A Deep Learning Approach to Jet Tagging Utilizing Novel Graph Construction Methods and Chebyshev Graph Convolutions
by: Semlani, Yash, et al.
Published: (2023)

Exploration of Parameter Spaces Assisted by Machine Learning
by: Hammad, A., et al.
Published: (2022)

Machine-Learning-Inspired SMEFT Simplified Template Cross Sections: A Case Study in ZH Production
by: Conde, Daniel, et al.
Published: (2026)

Lecture notes on Machine Learning applications for global fits
by: Alda, Jorge
Published: (2026)

Exploring the Truth and Beauty of Theory Landscapes with Machine Learning
by: Matchev, Konstantin T., et al.
Published: (2024)

Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination
by: Jahin, Md Abrar, et al.
Published: (2024)

Reconstruction of boosted and resolved multi-Higgs-boson events with symmetry-preserving attention networks
by: Li, Haoyang, et al.
Published: (2024)

Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer
by: Zhang, Jing-Zong, et al.
Published: (2025)

PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction
by: Lu, Junjian, et al.
Published: (2024)

Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
by: Gambhir, Rikab, et al.
Published: (2024)

Advancing Physics Data Analysis through Machine Learning and Physics-Informed Neural Networks
by: Vatellis, Vasileios
Published: (2024)

Fermionic UV models for neutral triple gauge boson vertices
by: Cepedello, Ricardo, et al.
Published: (2024)

Machine Learning Neutrino-Nucleus Cross Sections
by: Hackett, Daniel C., et al.
Published: (2024)

Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics
by: Aarts, Gert, et al.
Published: (2025)

Uncovering Singularities in Feynman Integrals via Machine Learning
by: Liu, Yuanche, et al.
Published: (2025)

TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches
by: Oleksiyuk, Ivan, et al.
Published: (2025)

The Pareto Frontier of Resilient Jet Tagging
by: Gambhir, Rikab, et al.
Published: (2025)

E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features
by: Islam, Md Raqibul, et al.
Published: (2025)

Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
by: Konar, Partha, et al.
Published: (2025)

SEAL - A Symmetry EncourAging Loss for High Energy Physics
by: Hebbar, Pradyun, et al.
Published: (2025)

Good flavor search in SU(5): a machine learning approach
by: Abu-Ajamieh, Fayez, et al.
Published: (2025)

Simulation-Prior Independent Neural Unfolding Procedure
by: Butter, Anja, et al.
Published: (2025)