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Anya Butter (Heidelberg University) “Big Data Techniques for Precision Simulations and Optimal Observables”

September 23, 2021 @ 12:00 pm - 1:00 pm

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 simulations and analyses to extract all relevant information.
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.
At the analysis level, machine learning methods have already shown impressive performance boosts for instance in top tagging and jet calibration. While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. Symbolic regression trained on matrix-element information provides optimal LHC observables in an easily interpretable form.


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Talk Recording Link:

Details

Date:
September 23, 2021
Time:
12:00 pm - 1:00 pm
Website:
https://lbnl.zoom.us/rec/share/bkGaO1SiEu7KfrTbPgwxaCnDAhZJ3Aoq1pCx1G-u6WMdK4vfzrkTT77KYUzuG59r.zr9cgzn3tTVN2Myh?startTime=1632422647000

Venue

Zoom Talk
50A-5132
Berkeley, ca 94720 + Google Map