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Deep learning for inferring cause of data anomalies

Azzolini, V. and Borisyak, M. and Cerminara, G. and Derkach, D. and Franzoni, G. and De Guio, F. and Koval, O. and Pierini, M. and Pol, A. and Ratnikov, F. and Siroky, F. and Ustyuzhanin, A. and Vlimant, J.-R. (2018) Deep learning for inferring cause of data anomalies. Journal of Physics Conference Series, 1085 . Art. No. 042015. ISSN 1742-6588. doi:10.1088/1742-6596/1085/4/042015.

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Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. In this paper, the problem of identifying channels in which anomalies occurred is considered. We introduce a generic deep learning model and prove that, under reasonable assumptions, the model learns to identify 'channels' which are affected by an anomaly. Such model could be used for data quality manager cross-check and assistance and identifying good channels in anomalous data samples. The main novelty of the method is that the model does not require ground truth labels for each channel, only global flag is used. This effectively distinguishes the model from classical classification methods. Being applied to CMS data collected in the year 2010, this approach proves its ability to decompose anomaly by separate channels.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Pierini, M.0000-0003-1939-4268
Vlimant, J.-R.0000-0002-9705-101X
Additional Information:© 2018 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. The research leading to these results was partly supported by Russian Science Foundation under grant agreement No 17-72-20127.
Funding AgencyGrant Number
Russian Science Foundation17-72-20127
Record Number:CaltechAUTHORS:20190606-101559273
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Official Citation:V. Azzolini et al 2018 J. Phys.: Conf. Ser. 1085 042015
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96190
Deposited By: Tony Diaz
Deposited On:06 Jun 2019 22:05
Last Modified:12 Jul 2022 17:05

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