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SUMMARY:Seminario_CA
DTSTART;VALUE=DATE-TIME:20201105T230000Z
DTEND;VALUE=DATE-TIME:20201106T010000Z
DTSTAMP;VALUE=DATE-TIME:20260501T133522Z
UID:indico-event-1636@cern.ch
DESCRIPTION:Title:"Machine Learning and Multi-Parton Interactions in pp co
 llisions from RHIC to LHC energies"\n\nOver the last years\, Machine Learn
 ing (ML) tools have been successfully applied to a wealth of problems in h
 igh-energy physics. Supervised ML methods allow for significant improvemen
 ts in classification problems by taking into account correlations among ob
 servables and by learning the optimal selection from prepared samples. In 
 this talk\, we will discuss the application of ML for the extraction of th
 e average number of Multi-Parton Interactions (MPI) from pp data. Boosted 
 Decision Trees (BDT) are trained considering observables calculated with p
 rimary charged particles in minimum-bias pp collisions at $\\sqrt{s}=13$\\
 \,TeV simulated with Pythia 8.244 tune 4C. Simulations at lower center-of-
 mass energies ranging from $\\sqrt{s}=0.2$ up to 13\\\,TeV are processed w
 ith the trained BDT\, the target values are found to be consistent with th
 e expected MPI activity. Consistent results are also obtained in simulatio
 ns where MPI and color reconnection are not activated. The method is also 
 found to be robust against both the MPI and the hadonization models. Using
  the existing LHC data on transverse momentum spectra as a function of mul
 tiplicity in pp collisions at $\\sqrt{s}=5.02$\, 7 and 13\\\,TeV\, we extr
 act the average MPI (target variable) for minimum-bias pp collisions as we
 ll as the multiplicity dependence of MPI. The multiplicity dependent resul
 ts are compared with existing ALICE measurements sensitive to MPI. Finally
 \, we discuss the possibility of using ML in order to build an event class
 ifier with strong sensitivity to MPI.\n\n\nhttps://cern.zoom.us/j/94943190
 670?pwd=TEtseVQ1bEwxanBCeTZFY2lzNzdEdz09\n\nhttps://indico.nucleares.unam.
 mx/event/1636/
LOCATION:
URL:https://indico.nucleares.unam.mx/event/1636/
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