arXiv subjects

Leonardo Barreto

Publications and source records attributed to Leonardo Barreto.

3 recordsLinked to original sources

arXiv2024

Jet cone radius dependence of $R_{AA}$ and $v_2$ at PbPb 5.02 TeV from JEWEL+$\rm T_RENTo$+v-USPhydro

We combine, for the first time, event-by-event $\rm T_RENTo$ initial conditions with the relativistic viscous hydrodynamic model v-USPhydro and the Monte Carlo event generator JEWEL to make predictions for the nuclear modification factor $R_{AA}$ and jet azimuthal anisotropies $v_n\left\{2\right\}$ in $\sqrt{s_{NN}}=5.02 \, \rm TeV$ PbPb collisions for multiple centralities and values of the jet cone radius $R$. The $R$-dependence of $R_{AA}$ and $v_2\left\{2\right\}$ strongly depends on the presence of recoiling scattering centers. We find a small jet $v_3\left\{2\right\}$ in mid-central collisions and consistent results in wide jet $p_T$ regions and centralities with ATLAS data.

nucl-th↗

arXiv2023

$R$-dependence of jet observables with JEWEL+v-USPhydro

The $R$-dependence of jet observables provides a new tool in understanding the interplay between the jet energy-loss mechanism and medium response in heavy-ion collisions. This work applies the Monte Carlo events generator JEWEL and PYTHIA, coupled with $\rm T_{R}ENTo$ initial conditions and the state-of-the-art (2+1)D v-USPhydro, for the simulation of jet distributions and substructure observables for lead-lead collisions at LHC energy scales. We present the jet nuclear modification $R_{AA}$ and anisotropic flow coefficients $v_{n=2,3}$ varying the jet cone radius $R$, in the context of anti-$k_T$ jets, in addition to leading subjet fragmentation. The calculations indicate the impacts of the hydrodynamic evolution and weakly-coupled medium response, given by recoils, on the distributions. Results are compared to experimental data in a wide range of jet $p_T$ and collision centrality, and displayed along large jets ($R \ge 0.6$) predictions.

nucl-th↗

arXiv2019

Self-organized inductive reasoning with NeMuS

Neural Multi-Space (NeMuS) is a weighted multi-space representation for a portion of first-order logic designed for use with machine learning and neural network methods. It was demonstrated that it can be used to perform reasoning based on regions forming patterns of refutation and also in the process of inductive learning in ILP-like style. Initial experiments were carried out to investigate whether a self-organizing the approach is suitable to generate similar concept regions according to the attributes that form such concepts. We present the results and make an analysis of the suitability of the method in the process of inductive learning with NeMuS.

cs.AI↗