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             “Collision Course: Particle Physics meets Machine Learning"

 

        Date:

    Download-files:

      Time:

  Thursday, 18 Feb 2021

    Video-Recording for any system with MP4-support

   - Video.mp4  (ca.287 Mb)

   - Video_with_eng_sub.mp4  (ca. 287 Mb)

 

 15:15 – 16:15

 

 

Abstract:

 

Modern machine learning has had an outsized impact on many scientific fields,

and particle physics is no exception. What is special about particle physics,

though, is the vast amount of theoretical and experimental knowledge that we

already have about many problems in the field.  In this colloquium,

I present two cases studies involving quantum chromodynamics (QCD) at the

Large Hadron Collider (LHC), highlighting the fascinating interplay between

theoretical principles and machine learning strategies.  First, by cataloging the

space of all possible QCD measurements, we (re)discovered technology relevant

for self-driving cars. Second, by quantifying the similarity between two LHC

collisions, we unlocked a class of nonparametric machine learning techniques

based on optimal transport. In addition to providing new quantitative insights

into QCD, these techniques enable new ways to visualize data from the LHC.

 

Speaker today:    Jesse Thaler   (MIT)

 

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