7 papers
Propagating data noise through the fit: the Monte Carlo replica distribution
Mark N. Costantini
The Monte Carlo (MC) replica method quantifies parameter uncertainties in global fits of parton distribution functions (PDFs) and Standard Model Effective Field Theory (SMEFT) Wils…
A linear PDF model for Bayesian inference
Mark N. Costantini, Luca Mantani, James M. Moore +1
A robust uncertainty estimate in global analyses of Parton Distribution Functions (PDFs) is essential at the Large Hadron Collider (LHC), especially in view of the high-precision d…
Tailored PDFs for New Physics searches
Ella Cole, Mark N. Costantini, Elie Hammou +4
Given the non-negligible interplay between parton distribution functions (PDFs) at large x and potential New Physics (NP) effects in the high-energy tails of hadron collider observ…
Colibri: A new tool for fast-flying PDF fits
Mark N. Costantini, Luca Mantani, James M. Moore +2
We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use th…
Parton distributions confront LHC Run II data: a quantitative appraisal
Amedeo Chiefa, Mark N. Costantini, Juan Cruz-Martinez +6
We present a systematic comparison of theoretical predictions and various high-precision experimental measurements, specifically of differential cross sections performed by the LHC…
Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data
Andrea Barontini, Mark N. Costantini, Giovanni De Crescenzo +2
We critically assess the robustness of uncertainties on parton distribution functions (PDFs) determined using neural networks from global sets of experimental data collected from m…