Bayesian unfolding of charged particle $p_{\mathrm{T}} $ spectra with ALICE at the LHC

TL;DR

This paper introduces a Bayesian unfolding method to accurately reconstruct charged particle transverse momentum spectra in proton-proton collisions at the LHC, enabling detailed analysis of particle production mechanisms.

Contribution

The paper presents a novel Bayesian unfolding procedure for $p_{T}$ spectra correlated with charged-particle multiplicities, improving the analysis of collision data at the LHC.

Findings

Unfolded $p_{T}$ spectra in single multiplicity bins.

Derived moments of the $p_{T}$ distributions.

Validated the unfolding method with Monte Carlo simulations.

Abstract

The study of the Quark-Gluon Plasma created in ultrarelativistic heavy-ion collisions at the CERN-LHC is complemented by reference measurements in proton-lead (p--Pb) and proton-proton (pp) collisions, where the effects of multiple-parton interactions and hadronization beyond independent string fragmentation can be investigated. In these proceedings, we present a Bayesian unfolding procedure to reconstruct the correlation between transverse momentum ($ p_{\mathrm{T}}$) spectra of charged particles and the corresponding charged-particle multiplicities $N_{\mathrm{ch}}$. The unfolded spectra are presented in single multiplicity ($\Delta N_{\mathrm{ch}}$ = 1) bins and are used to derive moments of the $p_{\mathrm{T}} $ distributions. We illustrate the unfolding procedure of the $ p_{\mathrm{T}} $ spectra with a Monte Carlo simulation for pp collisions at a centre-of-mass energy of $\sqrt{\mathrm{s}}= 5.02$ TeV.