Cover Image

Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Péron, Lukas, Calafiura, Paolo, Ju, Xiangyang, Chan, Jay
Format:	Preprint
Published:	2025
Subjects:	High Energy Physics - Experiment Data Analysis, Statistics and Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Physics and Computing Performance of the EggNet Tracking Pipeline
by: Chan, Jay, et al.
Published: (2025)

EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
by: Calafiura, Paolo, et al.
Published: (2024)

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

Fast multilabel classification of HEP constraints with deep learning
by: Binjonaid, Maien
Published: (2024)

Graph Neural Network-based Tracking as a Service
by: Zhao, Haoran, et al.
Published: (2024)

Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
by: Chan, Jay
Published: (2026)

KAN we improve on HEP classification tasks? Kolmogorov-Arnold Networks applied to an LHC physics example
by: Erdmann, Johannes, et al.
Published: (2024)

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

An importance sampling method for Feldman-Cousins confidence intervals
by: Berns, Lukas
Published: (2023)

Uncertainty components in profile likelihood fits
by: Pinto, Andrés, et al.
Published: (2023)

Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
by: Qiu, Shikai, et al.
Published: (2023)

Systematic Uncertainties in Unfolding Considering the Likelihood Formalism
by: Brenner, Lydia, et al.
Published: (2025)

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

Data reduction strategy in the PandaX-4T experiment
by: Zhou, Yubo, et al.
Published: (2023)

Under-coverage in high-statistics counting experiments with finite MC samples
by: Alexe, Cristina-Andreea, et al.
Published: (2024)

Post-hoc regularisation of unfolded cross-section measurements
by: Koch, Lukas
Published: (2022)

FunTuple: A new N-tuple component for offline data processing at the LHCb experiment
by: Mathad, Abhijit, et al.
Published: (2023)

Monte Carlo method for constructing confidence intervals with unconstrained and constrained nuisance parameters in the NOvA experiment
by: Acero, M. A., et al.
Published: (2022)

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

A unified machine learning approach for reconstructing hadronically decaying tau leptons
by: Tani, Laurits, et al.
Published: (2024)

RootInteractive tool for multidimensional statistical analysis, machine learning and analytical model validation
by: Ivanov, Marian, et al.
Published: (2024)

Accelerating HEP simulations with Neural Importance Sampling
by: Deutschmann, Nicolas, et al.
Published: (2024)

Development of an LLM-Based System for Automatic Code Generation from HEP Publications
by: Saito, Masahiko, et al.
Published: (2026)

FAIR Universe HiggsML Uncertainty Dataset and Competition
by: Benato, Lisa, et al.
Published: (2024)

The asymptotic distribution of the likelihood ratio test statistic in two-peak discovery experiments
by: Salucci, Clara Bertinelli, et al.
Published: (2025)

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

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
by: Messuti, Giovanni, et al.
Published: (2024)

Frequentist Uncertainties on Neural Density Ratios with wifi Ensembles
by: Benevedes, Sean, et al.
Published: (2025)

Gradient-descent-based reconstruction for muon tomography based on automatic differentiation in PyTorch
by: Alameddine, Jean-Marco, et al.
Published: (2025)

Improved Asymptotic Formulae for Statistical Interpretation Based on Likelihood Ratio Tests
by: Xia, Li-Gang, et al.
Published: (2021)

Exotic and physics-informed support vector machines for high energy physics
by: Ramirez-Morales, A., et al.
Published: (2024)

Cosmic-ray tomography of shipping containers: A combination of complementary secondary particle and muon information using simulations
by: Prada, Maximilian Pérez, et al.
Published: (2025)

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

Normalizing Flows for Domain Adaptation when Identifying $Λ$ Hyperon Events
by: Kelleher, Rowan, et al.
Published: (2024)

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

Wasserstein normalized autoencoder for anomaly detection
by: CMS Collaboration
Published: (2025)

A Statistical Method for Improving Momentum Measurement of Photon Conversions Reconstructed from Single Electrons
by: Bingül, Ahmet, et al.
Published: (2024)

Deep Probabilistic Direction Prediction in 3D with Applications to Directional Dark Matter Detectors
by: Ghrear, Majd, et al.
Published: (2024)

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

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