A. W Thomas | Searcharxiv

arXiv2024

On the resolution of the sign of gluon polarization in the proton

Recently the possible existence of negative gluon helicity, $Δg$, has been observed to be compatible with existing empirical constraints, including from jet production in polarized proton-proton collisions at RHIC, and lattice QCD data on polarized gluon Ioffe time distributions. We perform a new global analysis of polarized parton distributions in the proton with new constraints from the high-$x$ region of deep-inelastic scattering (DIS). A dramatic reduction in the quality of the fit for the negative $Δg$ replicas compared to those with positive $Δg$ suggest that the negative $Δg$ solution cannot simultaneously account for high-$x$ polarized DIS data along with lattice and polarized jet data.

hep-ph↗

arXiv2023

Accelerating Markov Chain Monte Carlo sampling with diffusion models

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 MCMC run. Our approach leads to a significant reduction in the number of likelihood evaluations required to obtain an accurate representation of the Bayesian posterior across several analytic functions, as well as for a physical example based on a global analysis of parton distribution functions. Our method is extensible to other MCMC techniques, and we briefly compare our method to similar approaches based on normalizing flows. A code implementation can be found at https://github.com/NickHunt-Smith/MCMC-diffusion.

hep-ph↗