429 citations · 581 across the 9 of their papers we have counts for
9 papers
Regularising experimental correlations in LHC data: theory and application to a global analysis of parton distributions
Zahari Kassabov, Emanuele R. Nocera, Michael Wilson
We show how an inaccurate determination of experimental uncertainty correlations in high-precision LHC measurements may undermine the reliability of the associated . We formul…
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up
Luigi Del Debbio, Joe Marsh Rossney, Michael Wilson
A trivializing map is a field transformation whose Jacobian determinant exactly cancels the interaction terms in the action, providing a representation of the theory in terms of a…
Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing
Luigi Del Debbio, Tommaso Giani, Michael Wilson
We discuss the Bayesian approach to the solution of inverse problems and apply the formalism to analyse the closure tests performed by the NNPDF collaboration. Starting from a comp…
An open-source machine learning framework for global analyses of parton distributions
Richard D. Ball, Stefano Carrazza, Juan Cruz-Martinez +14
We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is acc…
The Path to Proton Structure at One-Percent Accuracy
Richard D. Ball, Stefano Carrazza, Juan Cruz-Martinez +14
We present a new set of parton distribution functions (PDFs) based on a fully global dataset and machine learning techniques: NNPDF4.0. We expand the NNPDF3.1 determination with 44…
Efficient Modelling of Trivializing Maps for Lattice Theory Using Normalizing Flows: A First Look at Scalability
Luigi Del Debbio, Joe Marsh Rossney, Michael Wilson
General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point. Recently…