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18-19 June 2018
Fermilab, Wilson Hall
US/Central timezone

Machine Learning in DQM at CMS Experiment

Jun 18, 2018, 3:45 PM
15m
One West (Fermilab, Wilson Hall)

One West

Fermilab, Wilson Hall

Oral Presentation Collider Physics

Speaker

Mr Guillermo Fidalgo (University of Puerto Rico Mayaguez)

Description

The Data Quality Monitoring (DQM) of CMS is a key asset to deliver high-quality data for physics analysis and it is used both in the online and offline environment. The current paradigm of the quality assessment is labor intensive and it is based on the scrutiny of a large number of histograms by detector experts comparing them with a reference. This project aims at applying recent progress in Machine Learning techniques to the automation of the DQM scrutiny. In particular the use of convolutional neural networks to spot problems in the acquired data is presented with particular attention to semi-supervised models (e.g. autoencoders) to define a classification strategy that doesn’t assume previous knowledge of failure modes. Real data from the hadron calorimeter of CMS are used to demonstrate the effectiveness of the proposed approach.

Primary author

Mr Guillermo Fidalgo (University of Puerto Rico Mayaguez)

Presentation Materials