DNN predictions for pp reference $p_\mathrm{T}$ spectra at unmeasured $\sqrt{s}$

TL;DR

This paper introduces a deep neural network model trained on ALICE data to interpolate and extrapolate proton-proton transverse-momentum spectra at unmeasured energies, aiding Quark-Gluon Plasma studies.

Contribution

The novel approach uses deep learning to predict pp spectra at energies beyond current measurements, improving reference data for heavy-ion collision analysis.

Findings

Model accurately interpolates existing data

Provides reliable predictions for future LHC energies

Enhances the precision of Quark-Gluon Plasma studies

Abstract

Studies of the properties of the Quark-Gluon Plasma in high-energy heavy-ion collisions commonly facilitate proton-proton (pp) collisions at the same center-of-mass energy per nucleon pair as a reference measurement. In this paper, a deep neural network-based approach for interpolating and extrapolating pp reference transverse-momentum spectra to unmeasured energies is presented. The model is trained with ALICE data from LHC Runs 1 and 2 and provides predictions for center-of-mass energies relevant to LHC Run 3 and beyond.