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Found 17 papers, 5 papers with code
Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions.
Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
We explore the performance of the extended capability of Spa-Net in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson.
Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
Generation of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive.
Geometry-aware Autoregressive Models for Calorimeter Shower Simulations
Calorimeter shower simulations are often the bottleneck in simulation time for particle physics detectors.
Machine-Learning Compression for Particle Physics Discoveries
In collider-based particle and nuclear physics experiments, data are produced at such extreme rates that only a subset can be recorded for later analysis.
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community.
SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention
The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics.
Feasibility of Correlated Extensive Air Shower Detection with a Distributed Cosmic Ray Network
We explore the sensitivity offered by a global network of cosmic ray detectors to a novel, unobserved phenomena: widely separated simultaneous extended air showers.
High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics
How to GAN Higher Jet Resolution
QCD-jets at the LHC are described by simple physics principles.
Super-Resolution
High Energy Physics - Phenomenology
Learning to Identify Electrons
We investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information.
Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques.
Modeling Smooth Backgrounds and Generic Localized Signals with Gaussian Processes
We demonstrate the application of this approach to modeling the background to searches for dijet resonances at the Large Hadron Collider and describe how the approach can be used in the search for generic localized signals.
Data Analysis, Statistics and Probability High Energy Physics - Experiment High Energy Physics - Phenomenology
Decorrelated Jet Substructure Tagging using Adversarial Neural Networks
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.
Parameterized Machine Learning for High-Energy Physics
We investigate a new structure for machine learning classifiers applied to problems in high-energy physics by expanding the inputs to include not only measured features but also physics parameters.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Searching for Higgs Boson Decay Modes with Deep Learning
Particle colliders enable us to probe the fundamental nature of matter by observing exotic particles produced by high-energy collisions.
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning
The Higgs boson is thought to provide the interaction that imparts mass to the fundamental fermions, but while measurements at the Large Hadron Collider (LHC) are consistent with this hypothesis, current analysis techniques lack the statistical power to cross the traditional 5$\sigma$ significance barrier without more data.
Searching for Exotic Particles in High-Energy Physics with Deep Learning
Standard approaches have relied on `shallow' machine learning models that have a limited capacity to learn complex non-linear functions of the inputs, and rely on a pain-staking search through manually constructed non-linear features.
High Energy Physics - Phenomenology High Energy Physics - Experiment
