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SUMMARY:Automated Data/MC Tuning via Phase-Space Optimization
DTSTART;VALUE=DATE-TIME:20260908T074000Z
DTEND;VALUE=DATE-TIME:20260908T081000Z
DTSTAMP;VALUE=DATE-TIME:20260922T191357Z
UID:indico-contribution-2035@events.ncbj.gov.pl
DESCRIPTION:Speakers: Piotr Kalaczyński (Astrocent\, CEAI AGH)\nHigh-fide
 lity Monte Carlo simulations (e.g.\, Geant4\, CORSIKA\, Pythia) are indisp
 ensable in high-energy and medical physics. However\, aligning simulated a
 nd experimental distributions remains a laborious task\, traditionally rel
 ying on manual selection tuning or computationally expensive grid scans. T
 his challenge is compounded when quality selection cuts must be jointly op
 timized across a high-dimensional phase space containing correlated detect
 or observables.\n\nAn automated\, domain-independent framework designed to
  efficiently optimize multi-variable quality selection criteria is introdu
 ced. Utilizing a pure-Julia architecture leveraging automated optimization
  engines\, the pipeline simultaneously minimizes binned shape discrepancie
 s while applying density-based regularizations to preserve overall statist
 ical efficiency. The methodology is evaluated using both synthetic and pub
 licly available data. The high performance of Julia's compilation ecosyste
 m enables end-to-end multi-dimensional optimization over millions of event
 s in seconds. Foreseen future applications of this framework include the o
 ptimization of complex data pipelines for large-scale neutrino observatori
 es like KM3NeT\, as well as next-generation medical workflows such as the 
 3Dπ liquid argon PET scanner. The resulting open-source toolkit provides 
 an effective approach to simulation tuning for both complex physical event
  analyses and precision medical imaging workflows.\n\nhttps://events.ncbj.
 gov.pl/event/468/contributions/2035/
LOCATION:
URL:https://events.ncbj.gov.pl/event/468/contributions/2035/
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