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Statut Confirmé
Domaines hep-ph
Date Vendredi 27 Novembre 2020
Heure 14:00
Institut LPTHE
Salle zoom room
Nom de l'orateur Butter
Prenom de l'orateur Anja
Addresse email de l'orateur
Institution de l'orateur Heidelberg U.
Titre Simulating LHC events with generative networks
Résumé 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.
Numéro de preprint arXiv
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