Reweighting and unweighting of parton distributions and the LHC W lepton asymmetry data

By N. C. R. D. Ball, Valerio Bertone, F. Cerutti, Luigi Del Debbio, S. Forte, Alberto Guffanti, Nathan Hartland, José I. Latorre, Juan Rojo, M. Ubiali. 2011

170 citations recorded by the source corpus.

We develop in more detail our reweighting method for incorporating new datasets in parton fits based on a Monte Carlo representation of PDFs. After revisiting the derivation of the reweighting formula, we show how to construct an unweighted PDF replica set which is statistically equivalent to a given reweighted set. We then use reweighting followed by unweighting to test the consistency of the method, specifically by verifying that results do not depend on the order in which new data are included in the fit via reweighting. We apply the reweighting method to study the impact of LHC W lepton asymmetry data on the NNPDF2.1 set. We show how these data reduce the PDF uncertainties of light quarks in the medium and small x region, providing the first solid constraints on PDFs from LHC data.

Subfields: Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/4e9562948391c02b2e7e0c07c204e9f98c064d50

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Nuclear and High Energy Physics

Citations in the corpus

The PDF4LHC report on PDFs and LHC data: Results from Run I and preparation for Run II (2015, methodology): “23 For Monte Carlo sets instead one should use the Bayesian reweighting method [216, 217].”
Parton distribution benchmarking with LHC data (2012, methodology): “[18] it is shown that once the LHC W asymmetry data is included in MSTW08 using PDF reweighting [66, 67], the fit quality improves substantially.”