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SUMMARY:Anja Butter (ITP in Heidelberg)
DTSTART:20220503T183000Z
DTEND:20220503T193000Z
DTSTAMP:20260423T024531Z
UID:nhetc/40
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/nhetc/40/">N
 ormalizing Flows for LHC Theory</a>\nby Anja Butter (ITP in Heidelberg) as
  part of NHETC Seminar\n\n\nAbstract\nOver the next years\, measurements a
 t the LHC and the HL-LHC will provide us with a wealth of new data. The be
 st hope to answer fundamental questions\, like the nature of dark matter\,
  is to adopt big data techniques in simulations and analyses to extract al
 l relevant information. On the theory side\, LHC physics crucially relies 
 on our ability to simulate events efficiently from first principles. These
  simulations will face unprecedented precision requirements to match the e
 xperimental accuracy. Innovative ML techniques like generative models can 
 help us overcome limitations from the high dimensionality of the phase spa
 ce. Such networks can be employed to directly simulate events or to suppor
 t first principle calculations like multi-loop amplitudes. Since neural ne
 tworks can be inverted\, they open new avenues in LHC analyses.\n
LOCATION:https://researchseminars.org/talk/nhetc/40/
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