Pantheon SEMPARIS Le serveur des séminaires parisiens Paris

Status Confirmed
Seminar Series LPTHE-PPH
Subjects hep-ph
Date Friday 27 November 2020
Time 14:00
Institute LPTHE
Seminar Room zoom room
Speaker's Last Name Butter
Speaker's First Name Anja
Speaker's Email Address
Speaker's Institution Heidelberg U.
Title Simulating LHC events with generative networks
Abstract Over the next years, measurements at the LHC and the HL-LHC will provide us with a wealth of data. The best hope of answering fundamental questions like the nature of dark matter, is to adopt big data techniques in analyses and simulations to extract all relevant information. At the analysis level, machine learning methods have already shown impressive performance boosts in many areas like top tagging, jet calibration or particle identification. On the theory side, LHC physics crucially relies on our ability to simulate events efficiently from first principles. In the coming LHC runs, these simulations will face unprecedented precision requirements to match the experimental accuracy. Innovative ML techniques like generative models can help us overcome limitations from the high dimensionality of the parameter space. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they also open new avenues in LHC analyses.
arXiv Preprint Number
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