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Talk1 h 4 min

Learning to discover: machine learning in high-energy physics

Balázs Kégl · CERN

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About

In this talk we will survey some of the latest developments in machine learning research through the optics of potential applications in high-energy physics. We will then describe three ongoing projects in detail. The main subject of the talk is the data challenge we are organizing with ATLAS on optimizing the discovery significance for the Higgs to tau-tau channel. Second, we describe our collaboration with the LHCb experiment on designing and optimizing fast multi-variate techniques that can be implemented as online classifiers in triggers. Finally, we will sketch a relatively young project with the ILC (Calice) group in which we are attempting to apply deep learning techniques for inference on imaging calorimeter data..

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The talk

  1. Learning to discover: machine learning in high-energy physics 2014-05-13 · 1 h 4 min · CERN's player

Credits and licence

Institution
CERN
Department
EP Seminar
Speaker
Balázs Kégl
Year
2014
Language
English
Subjects
Physics, Computer science, Earth and environment
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
Who can watch
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