Filip Široký

Bachelor's thesis

Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data

Anomaly Detection Using Deep Sparse Autoencoders for CERN Particle Detector Data
Abstract:
The certification of the Compact Muon Solenoid (CMS) particle detector data, as used for physics analysis, is a crucial task to ensure the quality of all physics results published by CERN. Currently, the certification conducted by human experts is labour intensive and can only be segmented on a long period of time basis. This contribution focuses on the design and prototype of an automated certification …more
Abstract:
The certification of the Compact Muon Solenoid (CMS) particle detector data, as used for physics analysis, is a crucial task to ensure the quality of all physics results published by CERN. Currently, the certification conducted by human experts is labour intensive and can only be segmented on a long period of time basis. This contribution focuses on the design and prototype of an automated certification …more
 
 
Language used: English
Date on which the thesis was submitted / produced: 27. 5. 2019

Thesis defence

  • Date of defence: 25. 6. 2019
  • Supervisor: doc. RNDr. Petr Sojka, Ph.D.
  • Reader: RNDr. Petr Novotný, Ph.D., Giovanni Franzoni, Ph.D.

Citation record

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Institution archiving the thesis and making it accessible: Masarykova univerzita, Fakulta informatiky