Search Results for author:
Found 16 papers, 7 papers with code
Combined track finding with GNN & CKF
The application of Graph Neural Networks (GNN) in track reconstruction is a promising approach to cope with the challenges arising at the High-Luminosity upgrade of the Large Hadron Collider (HL-LHC).
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
Finetuning Foundation Models for Joint Analysis Optimization
In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components.
Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics
We propose to apply several gradient estimation techniques to enable the differentiation of programs with discrete randomness in High Energy Physics.
Hierarchical Neural Simulation-Based Inference Over Event Ensembles
When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest.
Configurable calorimeter simulation for AI applications
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator.
FAIR for AI: An interdisciplinary and international community building perspective
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
neos: End-to-End-Optimised Summary Statistics for High Energy Physics
The advent of deep learning has yielded powerful tools to automatically compute gradients of computations.
Differentiable Matrix Elements with MadJax
MadJax is a tool for generating and evaluating differentiable matrix elements of high energy scattering processes.
Etalumis: Bringing Probabilistic Programming to Scientific Simulators at Scale
Probabilistic programming languages (PPLs) are receiving widespread attention for performing Bayesian inference in complex generative models.
Constraining $A_4$ Leptonic Flavour Model Parameters at Colliders and Beyond
We constrain the model parameters of a generic $A_4$ leptonic flavour model using a synergy of experimental data including limits from charged lepton flavour conversion, an 8 TeV collider analysis and constraints from the anomalous magnetic moment of the muon.
High Energy Physics - Phenomenology
Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to record and control random number draws within simulators in a language-agnostic way.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators
We consider the problem of Bayesian inference in the family of probabilistic models implicitly defined by stochastic generative models of data.
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
HEPData: a repository for high energy physics data
The Durham High Energy Physics Database (HEPData) has been built up over the past four decades as a unique open-access repository for scattering data from experimental particle physics papers.
High Energy Physics - Experiment Digital Libraries High Energy Physics - Phenomenology
