Abstract

Abstract We investigate the Monte Carlo approach to propagation of experimental uncertainties within the context of the established “MSTW 2008” global analysis of parton distribution functions (PDFs) of the proton at next-to-leading order in the strong coupling. We show that the Monte Carlo approach using replicas of the original data gives PDF uncertainties in good agreement with the usual Hessian approach using the standard Δχ 2 = 1 criterion, then we explore potential parameterisation bias by increasing the number of free parameters, concluding that any parameterisation bias is likely to be small, with the exception of the valence-quark distributions at low momentum fractions x. We motivate the need for a larger tolerance, Δχ 2 > 1, by making fits to restricted data sets and idealised consistent or inconsistent pseudodata. Instead of using data replicas, we alternatively produce PDF sets randomly distributed according to the covariance matrix of fit parameters including appropriate tolerance values, then we demonstrate a simpler method to produce an arbitrary number of random predictions on-the-fly from the existing eigenvector PDF sets. Finally, as a simple example application, we use Bayesian reweighting to study the effect of recent LHC data on the lepton charge asymmetry from W boson decays.

Highlights

  • In this paper we make a first study of the Monte Carlo approach to experimental error propagation within the context of the established “MSTW 2008” parton distribution functions (PDFs) determination [6]

  • We investigate the Monte Carlo approach to propagation of experimental uncertainties within the context of the established “MSTW 2008” global analysis of parton distribution functions (PDFs) of the proton at next-to-leading order in the strong coupling

  • And 7 we show the effect of fitting the genuine data, the consistent or inconsistent idealised pseudodata, in each case using Monte Carlo (MC) error propagation with Nrep = 40 replica data sets and n = 20 input PDF parameters, and we compare to the standard MSTW 2008 next-to-leading order (NLO) fit with dynamic tolerance

Read more

Summary

Recap of the Hessian method

The basic procedure for propagating experimental uncertainties in global PDF analyses using the Hessian method is discussed in detail in refs. [6, 9,10,11]. It is perhaps inconsistent to assume Gaussian uncertainties in the replica generation with a quartic penalty term in the χ2: changing to a quadratic penalty term would allow more freedom in the fitted normalisations and so the PDF parameters would move less, likely reducing the PDF uncertainty compared to the. The Hessian error propagation via eigenvector PDF sets includes theoretical uncertainties on the hadronisation corrections for the CDF jet data (treated as a correlated systematic) and the small modification for the nuclear corrections (r1, r2, r3) [6]. The approximate equivalence between the Hessian and MC methods may break down, when fitting a limited selection of discrepant data sets that are insufficient to unambiguously constrain all fitted parameters

Investigation of potential parameterisation bias
Fits to restricted data sets using data replicas
Fits to idealised consistent and inconsistent pseudodata
Random PDFs generated in space of fit parameters
Findings
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call