*EXTRAORDINARIO, Notar día y hora*" "Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

Machine Learning (ML) techniques have been employed for the high energy
physics (HEP) community since the early 80s to deal with a broad
spectrum of problems. This work explores the prospects of using Deep
Learning techniques to estimate elliptic flow (v2) in heavy-ion
collisions at the RHIC and LHC energies. A novel method is developed to
process the input observables from track-level information. The
proposed DNN model is trained with Pb-Pb collisions at √sNN=5.02 TeV
minimum bias events simulated with AMPT model. The predictions from the
ML technique are compared to both simulation and experiment. The Deep
Learning model seems to preserve the centrality and energy dependence
of v2 for the LHC and RHIC energies. The DNN model is also quite
successful in predicting the pT dependence of v2. When subjected to
event simulation with additional noise, the proposed DNN model still
keeps the robustness and prediction accuracy intact up to a reasonable
extent.

Evento presencial transmitido por Zoom. Liga:
https://cuaieed-unam.zoom.us/j/82622280628?pwd=c1R1UWpjbDdtM0RwYTNkK09kdmthdz09

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