BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//INFN-LNF - ECPv5.14.2.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:INFN-LNF
X-ORIGINAL-URL:https://w3.lnf.infn.it
X-WR-CALDESC:Eventi per INFN-LNF
BEGIN:VTIMEZONE
TZID:Europe/Rome
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20200329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20201025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Rome:20200220T143000
DTEND;TZID=Europe/Rome:20200220T160000
DTSTAMP:20260422T044417
CREATED:20200115T184742Z
LAST-MODIFIED:20200227T184843Z
UID:22437-1582209000-1582214400@w3.lnf.infn.it
SUMMARY:Machine learning an unknown physical law: the structure of the proton
DESCRIPTION:Machine learning techniques are increasingly used for recognizing pattern and devising optimal strategies: situations in which the machine is taught (or teaches itself) to learn a known correct answer\, or the best use of known rules. In particle physics\, machine learning has been used now for several years  in order to determine an underlying physical law which is known to exist\, but which is unknown. Furthermore\, because elementary particles are quantum objects\, this law is stochastic in nature: the machine has to learn a probability distribution\, rather than a unique answer. I will discuss some classic results\, used among others in the discovery of the Higgs boson\, as well as recent developments\, which raise the fundamental question of how to decide whether an answer is correct.
URL:/event/machine-learning-an-unknown-physical-law-the-structure-of-the-proton/
LOCATION:Aula Salvini
CATEGORIES:Seminari generali
END:VEVENT
END:VCALENDAR