Deep Learning as a Parton Shower

Particle Physics Theory seminar

Deep Learning as a Parton Shower

  • Event time: 2:00pm
  • Event date: 23rd January 2019
  • Speaker: James Monk (University of Copenhagen)
  • Location: Higgs Centre Seminar Room, Room 4305,

Event details

There is an interesting similarity between the way that neural networks learn about data
 and the way that models of particle production are structured.  
This connection between neural networks and renormalisable theories may help explain 
why deep learning has been successful for a wide range of tasks.  
In this talk I will describe a neural network that has been structured so as to behave 
as a toy parton shower model for particle production, which may make some of the connections 
between deep learning and particle physics more explicit. By applying a shower merging procedure, 
the network can be evaluated on unshowered events produced by a matrix element calculation. 
The trained network behaves as a parton shower that qualitatively reproduces jet-based observables.

Event resources

About Particle Physics Theory seminars

The Particle Physics Theory seminar is a weekly series of talks reflecting the diverse interests of the group. Topics include analytic and numerical calculations based on the Standard Model of elementary particle physics, theories exploring new physics, as well as more formal developments in gauge theories and gravity..

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