6th Inter-experiment Machine Learning Workshop

Modeling Nch distributions and pT spectra in high-energy pp collisions with DNNs - Poster

31 Jan 2024, 17:05

5m

61/1-201 - Pas perdus - Not a meeting room - (CERN)

Poster (from contributed talk) 3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model Poster Session

Speaker

Maria Alejandra Calmon Behling (Goethe University Frankfurt (DE))

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.

Presentation materials

POSTER_IML24_Maria_v3_final.pdf