BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Study to characterize the production of antineutrons in high energ
 y $pp$ collisions through charge exchange interactions in the ALICE experi
 ment
DTSTART;VALUE=DATE-TIME:20261020T230000Z
DTEND;VALUE=DATE-TIME:20261020T231000Z
DTSTAMP;VALUE=DATE-TIME:20261103T232631Z
UID:indico-contribution-28@cern.ch
DESCRIPTION:Speakers: Ms. LUGO PORRAS\, Fabiola Viridiana (IF UNAM)\nIn a 
 previous study\, simulations were performed to evaluate the feasibility of
  antineutron ($\\bar{n}$) identification and kinematic reconstruction via 
 hadronic charge-exchange (CEX) interactions: $\\bar{n}+n\\rightarrow\\bar{
 p}+p$ and $\\bar{n}+n\\rightarrow\\bar{p}+p+\\pi^0$. The target neutrons a
 re those in the silicon nuclei of the inner tracking detectors used in LHC
  experiments such as ALICE\, ATLAS\, and CMS. GEANT4 simulations using the
  ALICE Inner Tracking System (ITS) as a working example were employed to d
 evelop a dedicated protocol for $\\bar{n}$ identification and kinematic re
 construction. The method focuses on identifying the proton partner from th
 e CEX interaction\, a challenging task due to the presence of fragmentatio
 n protons from silicon nuclei. Momentum correlations between the $\\bar{n}
 $ and all possible $\\bar{p}p$ pairs showed that successful partner identi
 fication and $\\bar{n}$ reconstruction are associated with events with min
 imal momentum loss. Based solely on $\\bar{p}p$ observables\, an efficienc
 y of 0.006% was estimated for PYTHIA-generated $pp$ collisions at $\\sqrt{
 s_{pp}}=13$ TeV.\n\nIn this work\, the proposed protocol is applied to Mon
 te Carlo $pp$ collision samples from ALICE Run 3 to evaluate its performan
 ce under more realistic detector conditions. A preliminary CEX identificat
 ion efficiency lower than that obtained in the feasibility study is obtain
 ed after incorporating the full ALICE detector geometry and background-sup
 pression criteria. These simulations also enable a detailed characterizati
 on of background processes that mimic the CEX signal. To improve signal-ba
 ckground discrimination\, classical one-dimensional selections are compare
 d with a multivariable approach based on machine learning. The machine-lea
 rning selection provides significantly better signal-background separation
 \, yielding a final CEX candidate sample with a purity of approximately 70
 %.\n\nhttps://indico.nucleares.unam.mx/event/2675/session/3/contribution/2
 8
LOCATION:Puebla
URL:https://indico.nucleares.unam.mx/event/2675/session/3/contribution/28
END:VEVENT
END:VCALENDAR