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SUMMARY:Anja Butter (ITP\, University of Heidelberg)
DTSTART:20220602T160000Z
DTEND:20220602T170000Z
DTSTAMP:20260423T003249Z
UID:MPML/75
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/75/">Ma
 chine Learning and LHC Event Generation</a>\nby Anja Butter (ITP\, Univers
 ity of Heidelberg) as part of Mathematics\, Physics and Machine Learning (
 IST\, Lisbon)\n\n\nAbstract\nFirst-principle simulations are at the heart 
 of the high-energy physics research program. They link the vast data outpu
 t of multi-purpose detectors with fundamental theory predictions and inter
 pretation. In the coming LHC runs\, these simulations will face unpreceden
 ted precision requirements to match the experimental accuracy. New ideas a
 nd tools based on neural networks have been developed at the interface of 
 particle physics and machine learning. They can improve the speed and prec
 ision of forward simulations and handle the complexity of collision data. 
 Such networks can be employed within established simulation tools or as pa
 rt of a new framework. Since neural networks can be inverted\, they open n
 ew avenues in LHC analyses.\n
LOCATION:https://researchseminars.org/talk/MPML/75/
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