Speaker
Description
During the data-taking campaigns Run 1 and Run 2 of the Large Hadron Collider (LHC), the ALICE collaboration collected a large amount of proton-proton (pp) collisions across a variety of center-of-mass energies ($\sqrt{s\,}$). This extensive dataset is well suited to study the energy dependence of particle production. Deep neural networks (DNNs) provide a powerful regression tool to capture underlying multidimensional correlations inherent in the data. In this contribution, DNNs are used to parameterize recent ALICE measurements of charged-particle multiplicity ($N_{\mathrm{ch}}$) distributions and transverse momentum ($p_{\mathrm{T}}$) spectra. The model architectures are defined and validated using a Bayesian-Optimization hyperparameter search on PYTHIA simulations for a wide $\sqrt{s\,}$ range and then trained on the ALICE data. An ensemble method is used to predict the observables of interest, extrapolating the measurements towards higher $N_{\mathrm{ch}}$ and $p_{\mathrm{T}}$ values as well as to unmeasured $\sqrt{s\,}$ from $0.5$ to $100\ \mathrm{TeV}$. We demonstrate that the predicted $p_{\mathrm{T}}$ spectra can serve as a reference for future heavy-ion measurements, e.g. the O–O campaign planned in LHC Run 3, where no dedicated pp data-taking at the same $\sqrt{s\,}$ is currently foreseen.
