BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Roger Huang (LBNL)\, Machine Learning Based Reweighting Methods in
  Neutrino Physics
DTSTART:20260827T190000Z
DTEND:20260827T210000Z
DTSTAMP:20260913T081400Z
UID:indico-event-33504@indico.bnl.gov
DESCRIPTION:Next-generation long-baseline neutrino oscillation experiments
  like DUNE and Hyper-Kamiokande will achieve orders of magnitude improveme
 nts in statistics compared to current long-baseline experiments. For them 
 to make their desired measurements of parameters like δ_CP\, this increas
 e in statistics will have to be accompanied by much stronger control of ma
 ny systematic uncertainties around neutrino-nucleus interactions. Machine 
 learning-based reweighting methods offer better ways to probe high-dimensi
 onal effects in our simulation and data\, providing a path to better under
 standing these uncertainties. This talk will present two such applications
  in neutrino physics. The first is with OmniFold\, a ML-based unfolding me
 thod that allows extraction of unbinned\, high-dimensional results\, appli
 ed to the T2K experiment. The second is with ML-based reweighting of the p
 redictions from neutrino event generators\, facilitating easier study of t
 he effects of different theoretical models when analyzing neutrino data in
  general.\n\nhttps://indico.bnl.gov/event/33504/
URL:https://indico.bnl.gov/event/33504/
END:VEVENT
END:VCALENDAR