Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Classical and Quantum gravitation (1)
- Collisions (1)
- Deep Inelastic Scattering or Small-x Physics (1)
- Diffusion model (1)
- Distribution (1)
-
- Distribution functions (1)
- Elementary particles (1)
- First principles (1)
- Flavor (particle physics) (1)
- Hadrons (1)
- Heavy ions (1)
- Jets and Jet Substructure (1)
- Large hadron collider (1)
- Markov Chain Monte Carlo (1)
- Nuclear matter (1)
- Particle accelerators (1)
- Parton distributions (1)
- Partons (1)
- Physics (1)
- Physics and astronomy (1)
- Physics of elementary particles and fields (1)
- Properties of hadrons (1)
- Quantum chromodynamics (1)
- Quantum field theories (1)
- Quantum field theory (1)
- Quantum physics (1)
- Quarks (1)
- Relativistic heavy ion collider (1)
- Relativity theory (1)
Articles 1 - 2 of 2
Full-Text Articles in Physical Sciences and Mathematics
Accelerating Markov Chain Monte Carlo Sampling With Diffusion Models, N. T. Hunt-Smith, W. Melnitchouk, F. Ringer, N. Sato, A. W. Thomas, M. J. White
Accelerating Markov Chain Monte Carlo Sampling With Diffusion Models, N. T. Hunt-Smith, W. Melnitchouk, F. Ringer, N. Sato, A. W. Thomas, M. J. White
Physics Faculty Publications
Global fits of physics models require efficient methods for exploring high-dimensional and/or multimodal posterior functions. We introduce a novel method for accelerating Markov Chain Monte Carlo (MCMC) sampling by pairing a Metropolis-Hastings algorithm with a diffusion model that can draw global samples with the aim of approximating the posterior. We briefly review diffusion models in the context of image synthesis before providing a streamlined diffusion model tailored towards low-dimensional data arrays. We then present our adapted Metropolis-Hastings algorithm which combines local proposals with global proposals taken from a diffusion model that is regularly trained on the samples produced during the …
Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan
Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan
Physics Faculty Publications
We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in …