Data-driven analysis of the beauty hadron production in p+p collisions at the LHC with Bayesian unfolding

TL;DR

This paper presents a Bayesian unfolding analysis of beauty hadron production in proton-proton collisions at the LHC, providing precise cross sections and insights into heavy flavour production mechanisms.

Contribution

It introduces a data-driven Bayesian unfolding method to recover full kinematic information of beauty hadrons from decay data, improving measurement precision.

Findings

Consistent beauty hadron production cross sections across decay channels.

Detailed pT and rapidity dependence of beauty production at 5.02 and 13 TeV.

Enhanced precision over previous measurements, constraining QCD models.

Abstract

Heavy flavour production in proton-proton (pp) collisions provides insights into the fundamental properties of Quantum Chromodynamics (QCD). Beauty hadron production measurements are widely performed through indirect approaches based on their inclusive decay modes. A Bayesian unfolding data-driven analysis of the ALICE and LHCb data was performed in this study, which recovers the full kinematic information of the beauty hadrons via different inclusive decay channels. The corresponding beauty hadron production cross sections obtained after the Bayesian unfolding are found to be consistent within their uncertainties. The weighted average open beauty production cross sections are presented as a function of the transverse momentum and rapidity in pp collisions at $\sqrt{s}$ = 5.02 TeV and $\sqrt{s}$ = 13 TeV, respectively. The $p_T$-integrated open beauty production $\mathrm{d}\sigma/\mathrm{d}y$ and the total $\mathrm{b}\rm\overline{b}$ cross section $\sigma_{\rm \mathrm{b}\rm\overline{b}}$ are also reported. The precision of these results significantly improves upon worldwide measurements, providing valuable validation and constraints on mechanisms of heavy flavour production in pp collisions at the LHC energies.