BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:Uncertainty Quantification in PDF Determinations: a Machine Learni
 ng Adventure
DTSTART:20260128T130000Z
DTEND:20260128T140000Z
DTSTAMP:20260317T012600Z
UID:indico-event-1616765@indico.cern.ch
DESCRIPTION:Speakers: Luigi Del Debbio (The University of Edinburgh (GB))\
 n\nPrecise and accurate theoretical predictions are a fundamental ingredie
 nt in order to exploit the full potential of the wealth of data from the L
 HC experiments. In this context\, it is particularly important to have a r
 obust determination of the uncertainties on the current determinations of 
 Parton Distribution Functions (PDFs). The NNPDF collaboration has pioneere
 d the usage of Machine Learning (ML) techniques in order to extract PDFs f
 rom a finite set of data\, a typical example of an inverse problem. In thi
 s talk we introduce a framework to analyse the training of Neural Networks
 \, present its application to the PDFs determination\, and discuss future 
 research directions. \n\nhttps://indico.cern.ch/event/1616765/\n\nZoom: h
 ttps://cern.zoom.us/j/67346292748?pwd=ZnRkQWh0ZXVFQTc5K256QW5NcEcrdz09
LOCATION:4/3-006 - TH Conference Room (CERN)
URL:https://indico.cern.ch/event/1616765/
END:VEVENT
END:VCALENDAR