Nuclear and Particle Physics

Faster, deeper, stronger: Machines learn particle physics

Name: Faster, deeper, stronger: Machines learn particle physics
Start: 2020-05-14T10:30:00+02:00
End: 2020-05-14T12:30:00+02:00
Location: Zoom

by Gregor Kasieczka (Universität Hamburg)

Thursday 14 May 2020, 10:30 → 12:30 Europe/Stockholm

634 7160 3574 (Zoom)

634 7160 3574

Zoom

Time: May 14, 2020 10:30 AM Stockholm Join Zoom Meeting https://uu-se.zoom.us/j/63471603574 Meeting ID: 634 7160 3574 One tap mobile +46850500828,,63471603574# Sweden +46850500829,,63471603574# Sweden Dial by your location +46 8 5050 0828 Sweden +46 8 5050 0829 Sweden +46 8 5052 0017 Sweden +46 850 539 728 Sweden +46 8 4468 2488 Sweden +45 32 71 31 57 Denmark +45 32 72 80 10 Denmark +45 32 72 80 11 Denmark +45 89 88 37 88 Denmark +45 32 70 12 06 Denmark +47 2396 0588 Norway +47 7349 4877 Norway +49 69 7104 9922 Germany +49 30 5679 5800 Germany +49 695 050 2596 Germany +33 1 7037 9729 France +33 1 7095 0103 France +33 1 7095 0350 France +33 7 5678 4048 France +33 1 7037 2246 France +44 203 481 5240 United Kingdom +44 208 080 6591 United Kingdom +44 208 080 6592 United Kingdom +44 330 088 5830 United Kingdom +44 131 460 1196 United Kingdom +44 203 481 5237 United Kingdom +1 669 900 6833 US (San Jose) +1 253 215 8782 US +1 301 715 8592 US +1 312 626 6799 US (Chicago) +1 346 248 7799 US (Houston) +1 408 652 8184 US (San Jose) +1 646 876 9923 US (New York) Meeting ID: 634 7160 3574 Find your local number: https://uu-se.zoom.us/u/cNamzXf Join by SIP 63471603574@109.105.112.236 63471603574@109.105.112.235 Join by H.323 109.105.112.236 109.105.112.235 Meeting ID: 634 7160 3574 Join by Skype for Business https://uu-se.zoom.us/skype/63471603574

Description

This will be a virtual seminar, and will be hosted in Zoom

Link: https://uu-se.zoom.us/j/63471603574

Meeting ID: 634 7160 3574

This meeting is password protected, if you are interested in joining, send an email to rebeca.gonzalez.suarez@physics.uu.se

Abstract:

Many experimental results from both particle and astrophysics hint that the Standard Model (SM) of particle physics cannot be a complete theory of Nature. However, in its first years of operation, the Large Hadron Collider at CERN was very successful in excluding large regions of parameter space for potential models beyond the SM. We present how deep learning can be used to search for deviations from the SM in a model independent way. Beyond searching for new physics, we explore ways to increase the robustness and understanding of network decisions and show how generative models can speed up simulations.