Double Descent and Overparameterization in Particle Physics Data
Fuente:
arXiv
Saved in:
| Main Authors: | Vigl, Matthias, Heinrich, Lukas |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Finetuning Foundation Models for Joint Analysis Optimization
by: Vigl, Matthias, et al.
Published: (2024)
by: Vigl, Matthias, et al.
Published: (2024)
Neural Scaling Laws for Boosted Jet Tagging
by: Vigl, Matthias, et al.
Published: (2026)
by: Vigl, Matthias, et al.
Published: (2026)
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)
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
by: Gandrakota, Abhijith, et al.
Published: (2024)
by: Gandrakota, Abhijith, et al.
Published: (2024)
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)
Particle Transformer for Jet Tagging
by: Qu, Huilin, et al.
Published: (2022)
by: Qu, Huilin, et al.
Published: (2022)
Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information
by: Birk, Joschka, et al.
Published: (2023)
by: Birk, Joschka, et al.
Published: (2023)
Flow Annealed Importance Sampling Bootstrap meets Differentiable Particle Physics
by: Kofler, Annalena, et al.
Published: (2024)
by: Kofler, Annalena, et al.
Published: (2024)
KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example
by: Erdmann, Johannes, et al.
Published: (2024)
by: Erdmann, Johannes, et al.
Published: (2024)
Universal New Physics Latent Space
by: Hallin, Anna, et al.
Published: (2024)
by: Hallin, Anna, et al.
Published: (2024)
One flow to correct them all: improving simulations in high-energy physics with a single normalising flow and a switch
by: Daumann, Caio Cesar, et al.
Published: (2024)
by: Daumann, Caio Cesar, et al.
Published: (2024)
Why Is Attention Sparse In Particle Transformer?
by: Legge, Timothy, et al.
Published: (2025)
by: Legge, Timothy, et al.
Published: (2025)
Seeing Double: Calibrating Two Jets at Once
by: Gambhir, Rikab, et al.
Published: (2024)
by: Gambhir, Rikab, et al.
Published: (2024)
Jet Tagging with More-Interaction Particle Transformer
by: Wu, Yifan, et al.
Published: (2024)
by: Wu, Yifan, et al.
Published: (2024)
Jet flavor tagging with Particle Transformer for Higgs factories
by: Suehara, Taikan, et al.
Published: (2026)
by: Suehara, Taikan, et al.
Published: (2026)
Neural simulation-based inference of the Higgs trilinear self-coupling via off-shell Higgs production
by: Ghosh, Aishik, et al.
Published: (2025)
by: Ghosh, Aishik, et al.
Published: (2025)
How to pick the best anomaly detector?
by: Hein, Marie, et al.
Published: (2025)
by: Hein, Marie, et al.
Published: (2025)
Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
by: Elsharkawy, Ibrahim, et al.
Published: (2025)
by: Elsharkawy, Ibrahim, et al.
Published: (2025)
SURFing to the Fundamental Limit of Jet Tagging
by: Pang, Ian, et al.
Published: (2025)
by: Pang, Ian, et al.
Published: (2025)
Enhancing next token prediction based pre-training for jet foundation models
by: Birk, Joschka, et al.
Published: (2025)
by: Birk, Joschka, et al.
Published: (2025)
Communicating Likelihoods with Normalising Flows
by: Araz, Jack Y., et al.
Published: (2025)
by: Araz, Jack Y., et al.
Published: (2025)
SIGMA: Single Interpolated Generative Model for Anomalies
by: Das, Ranit, et al.
Published: (2024)
by: Das, Ranit, et al.
Published: (2024)
FAIR Universe HiggsML Uncertainty Dataset and Competition
by: Benato, Lisa, et al.
Published: (2024)
by: Benato, Lisa, et al.
Published: (2024)
Learning Symmetry-Independent Jet Representations via Jet-Based Joint Embedding Predictive Architecture
by: Katel, Subash, et al.
Published: (2024)
by: Katel, Subash, et al.
Published: (2024)
Interpreting Transformers for Jet Tagging
by: Wang, Aaron, et al.
Published: (2024)
by: Wang, Aaron, 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)
by: Li, Haoyang, et al.
Published: (2024)
Neural Scaling Laws for Jet Generation
by: Amram, Oz, et al.
Published: (2026)
by: Amram, Oz, et al.
Published: (2026)
Moment Unfolding
by: Desai, Krish, et al.
Published: (2024)
by: Desai, Krish, et al.
Published: (2024)
OmniJet-$α$: The first cross-task foundation model for particle physics
by: Birk, Joschka, et al.
Published: (2024)
by: Birk, Joschka, et al.
Published: (2024)
An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
by: Backes, Mathias, et al.
Published: (2022)
by: Backes, Mathias, et al.
Published: (2022)
Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods
by: Maître, Daniel, et al.
Published: (2024)
by: Maître, Daniel, et al.
Published: (2024)
Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
by: Chan, Jay
Published: (2026)
by: Chan, Jay
Published: (2026)
Agents of Discovery
by: Diefenbacher, Sascha, et al.
Published: (2025)
by: Diefenbacher, Sascha, et al.
Published: (2025)
On Focusing Statistical Power for Searches and Measurements in Particle Physics
by: Carzon, James, et al.
Published: (2025)
by: Carzon, James, et al.
Published: (2025)
Transforming Simulation to Data Without Pairing
by: Gendreau-Distler, Eli, et al.
Published: (2025)
by: Gendreau-Distler, Eli, et al.
Published: (2025)
On the Statistical Interpretation of Discoveries in LHC Data
by: Chekanov, S. V., et al.
Published: (2026)
by: Chekanov, S. V., et al.
Published: (2026)
FlexCAST: Enabling Flexible Scientific Data Analyses
by: Nachman, Benjamin, et al.
Published: (2025)
by: Nachman, Benjamin, et al.
Published: (2025)
Physics and Computing Performance of the EggNet Tracking Pipeline
by: Chan, Jay, et al.
Published: (2025)
by: Chan, Jay, et al.
Published: (2025)
Anomaly Detection in Collider Physics via Factorized Observables
by: Metodiev, Eric M., et al.
Published: (2023)
by: Metodiev, Eric M., et al.
Published: (2023)
Machine Learning-based Unfolding for Cross Section Measurements in the Presence of Nuisance Parameters
by: Zhu, Huanbiao, et al.
Published: (2025)
by: Zhu, Huanbiao, et al.
Published: (2025)
Similar Items
-
Finetuning Foundation Models for Joint Analysis Optimization
by: Vigl, Matthias, et al.
Published: (2024) -
Neural Scaling Laws for Boosted Jet Tagging
by: Vigl, Matthias, et al.
Published: (2026) -
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
by: Golling, Tobias, et al.
Published: (2024) -
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
by: Gandrakota, Abhijith, et al.
Published: (2024) -
Cross-Domain Transfer with Particle Physics Foundation Models: From Jets to Neutrino Interactions
by: Krzmanc, Gregor, et al.
Published: (2026)