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<div class="">Dear all,</div>
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<div class="">Tomorrow we will have a theory seminar, please see below for details:</div>
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<div class="">Bluejeans connection: <a href="https://bluejeans.com/801786278" class="">https://bluejeans.com/801786278</a></div>
<div class="">Date and time: Monday April 19, 2021, 1:00PM EDT</div>
<div class="">Speaker: Anja Butter (University of Heidelberg)</div>
<div class="">Title: Simulating and unfolding LHC events with generative networks</div>
<div class="">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.</div>
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<div class="">The recording archives of our seminars can be found here:</div>
<div class=""> <a href="https://www.jlab.org/theory/seminars/2021seminars" class="">https://www.jlab.org/theory/seminars/2021seminars</a></div>
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<div class="">See you all there!</div>
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<div class="">Best regards,</div>
<div class="">Astrid, Christos, Filippo</div>
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