相关论文: Markov chain Monte Carlo (MCMC) based Likelihood E…

A likelihood analysis of the observables in deeply virtual exclusive photoproduction off a proton target, $ep \rightarrow e' p' \gamma'$, is presented. Two processes contribute to the reaction: deeply virtual Compton scattering, where the…

The extraction of Compton Form Factors (CFFs) in a global analysis of almost all Deeply Virtual Compton Scattering (DVCS) proton data is presented. The extracted quantities are DVCS sub-amplitudes and the most basic observables which are…

A framework defining benchmarks for the analysis of polarized exclusive scattering cross sections is proposed that uses physics symmetry constraints as well as lattice QCD predictions. These constraints are built into machine learning (ML)…

We consider conditional tests for non-negative discrete exponential families. We develop two Markov Chain Monte Carlo (MCMC) algorithms which allow us to sample from the conditional space and to perform approximated tests. The first…

We present a Machine Learning based approach to the cross section and asymmetries for deeply virtual Compton scattering from an unpolarized proton target using both an unpolarized and polarized electron beam. Machine learning methods are…

Markov chain Monte Carlo (MCMC) methods provide powerful framework for sampling unknown probability measures across a wide range of scientific applications. In some settings, the target distribution is supported on a lower-dimensional…

We address the problem of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) techniques. Such a task presents two interesting problems. First, the construction of the MCMC scheme should…

We present the results of a fitter code which aims at extracting Compton Form Factors (CFFs) from DVCS (Deep Virtual Compton Scattering) experimental data, in a largely model-independent way. CFFs are linked to GPDs (Generalized parton…

We develop a framework to establish benchmarks for machine learning and deep neural networks analyses of exclusive scattering cross sections (FemtoNet). Within this framework we present an extraction of Compton form factors for deeply…

We present an analysis of parton distribution functions (PDFs) of the proton using Markov Chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes' theorem and thus provides a means to directly sample the underlying…

We investigate the exercise of locally extracting the real and imaginary parts of the four twist-2 Compton form factors (CFFs) $\{\mathcal{H},\mathcal{E},\widetilde{\mathcal{H}},\widetilde{\mathcal{E}}\}$ which arise in the deeply virtual…

We develop a new methodology for extracting Compton form factors (CFFs) in from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse…

We systematically evaluate observables for hard exclusive electroproduction of real photons and compare them to experiment using a set of Generalized Parton Distributions (GPDs) whose parameters are constrained by Deeply Virtual Meson…

Markov Chain Monte Carlo (MCMC) requires to evaluate the full data likelihood at different parameter values iteratively and is often computationally infeasible for large data sets. In this paper, we propose to approximate the log-likelihood…

Markov chain Monte Carlo (MCMC) is a sampling-based method for estimating features of probability distributions. MCMC methods produce a serially correlated, yet representative, sample from the desired distribution. As such it can be…

We use a generalization of the Rosenbluth separation method for a model independent simultaneous extraction of the Compton Form Factors ${\cal H}$ and ${\cal E}$, from virtual Compton scattering data on an unpolarized target. A precise…

In this work we estimate the differential cross section for the high energy deeply virtual Compton scattering on a photon target within the QCD dipole-dipole scattering formalism. For the phenomenology, a saturation model for the…

We propose a multilevel Markov chain Monte Carlo (MCMC) method for the Bayesian inference of random field parameters in PDEs using high-resolution data. Compared to existing multilevel MCMC methods, we additionally consider level-dependent…

Inference after model selection presents computational challenges when dealing with intractable conditional distributions. Markov chain Monte Carlo (MCMC) is a common method for sampling from these distributions, but its slow convergence…

The Markov Chain Monte Carlo (MCMC) algorithm is a widely recognised as an efficient method for sampling a specified posterior distribution. However, when the posterior is multi-modal, conventional MCMC algorithms either tend to become…