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Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider

  • Adrian Alan Pol
  • Gianluca Cerminara
  • Cecile Germain
  • Maurizio Pierini
  • Agrima Seth
Original Article
  • 18 Downloads

Abstract

Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale high energy physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the data collected by the CMS muon detectors. We use deep neural networks to analyze LHC collision data, represented as images organized geographically. We train a classifier capable of detecting the known anomalous behaviors with unprecedented efficiency and explore the usage of convolutional autoencoders to extend anomaly detection capabilities to unforeseen failure modes. A generalization of this strategy could pave the way to the automation of the data quality assessment process for present and future high energy physics experiments.

Keywords

High energy physics Large Hadron Collider Compact Muon Solenoid Machine learning Data quality monitoring Artificial neural networks 

Notes

Acknowledgements

We thank the CMS collaboration for providing the data set used in this study. We are thankful to the members of the CMS Physics Performance and Data set project and the CMS DT Detector Performance Group for useful discussions, suggestions, and support. We acknowledge the support of the CMS CERN group for providing the computing resources to train our models and of CERN OpenLab for sponsoring A.S.’s internship at CERN, as part of the CERN OpenLab Summer student program. We thank Danilo Rezende for precious discussions and suggestions. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant agreement no. 772369).

References

  1. 1.
    The LHC Study Group (1995) The Large Hadron Collider, conceptual design. Technical report, CERN/AC/95-05 (LHC) GenevaGoogle Scholar
  2. 2.
    Chatrchyan S (2012) Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC. Phys Lett B 716(1):30–61ADSCrossRefGoogle Scholar
  3. 3.
    Khachatryan V (2015) Precise determination of the mass of the Higgs boson and tests of compatibility of its couplings with the standard model predictions using proton collisions at 7 and 8 TeV. Eur Phys J C 75(5):212ADSCrossRefGoogle Scholar
  4. 4.
    Chatrchyan S et al (2008) The CMS experiment at the CERN LHC. J Instrum Bristol 2006 Currens 3:S08004–1Google Scholar
  5. 5.
    Sirunyan AM et al (2018) Performance of the CMS muon detector and muon reconstruction with proton–proton collisions at \(\sqrt{s}= 13\,\text{tev}\). arXiv:1804.04528
  6. 6.
    De Guio F (2015) The data quality monitoring challenge at CMS: experience from first collisions and future plans. Technical report, CMS-CR-2015-329Google Scholar
  7. 7.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436ADSCrossRefGoogle Scholar
  8. 8.
    CMS collaboration (2010) Calibration of the CMS drift tube chambers and measurement of the drift velocity with cosmic rays. J Instrum 5(03):T03016Google Scholar
  9. 9.
    Tuura L, Eulisse G, Meyer A (2010) CMS data quality monitoring web service. J Phys Confer Ser (IOP Publishing) 219:072055Google Scholar
  10. 10.
    Borisyak M, Ratnikov F, Derkach D, Ustyuzhanin A (2017) Towards automation of data quality system for CERN CMS experiment. IOP Conf. Ser J Phys Confer Ser 898:092041.  https://doi.org/10.1088/1742-6596/898/9/092041
  11. 11.
    Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRefGoogle Scholar
  12. 12.
    Liu FT, Ting KM, Zhou ZH (2008) Isolation Forest. In: Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, IEEE Computer Society, Washington, DC, USA, pp 413-422.  https://doi.org/10.1109/ICDM.2008.17
  13. 13.
    Liu FT, Ting KM, Zhou Z-H (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data (TKDD) 6(1):3Google Scholar
  14. 14.
    Aggarwal CC (2015) Outlier analysis. Data mining. Springer, New York, pp 237–263Google Scholar
  15. 15.
    Aggarwal CC (2014) Data classification: algorithms and applications. CRC Press, Boca RatonCrossRefGoogle Scholar
  16. 16.
    Cowan G, Cranmer K, Gross E, Vitells O (2011) Asymptotic formulae for likelihood-based tests of new physics. Eur Phys J C 71(2):1554ADSCrossRefGoogle Scholar
  17. 17.
    Bengio Y, LeCun Y (2007) Scaling learning algorithms towards ai. Large-Scale Kernel Mach 34(5):1–41Google Scholar
  18. 18.
    Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828CrossRefGoogle Scholar
  19. 19.
    Goldstein M, Uchida S (2016) A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one 11(4):e0152173CrossRefGoogle Scholar
  20. 20.
    Zimek A, Schubert E, Kriegel H-P (2012) A survey on unsupervised outlier detection in high-dimensional numerical data. Statis Anal Data Mining ASA Data Sci J 5(5):363–387MathSciNetCrossRefGoogle Scholar
  21. 21.
    Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Proceedings of the 25th international conference on neural information processing systems, vol 1. Curran Associates Inc, Lake Tahoe, Nevada, pp 1097–1105Google Scholar
  22. 22.
    Rezende DJ, Mohamed S, Wierstra D (2014) Stochastic backpropagation and approximate inference in deep generative models. In: Xing EP, Jebara T (eds) Proceedings of the 31st international conference on machine learning, vol 32(2). PMLR, Bejing, China, pp 1278–1286Google Scholar
  23. 23.
    Tishby N, Zaslavsky N (2015) Deep learning and the information bottleneck principle. In: Proceedings of IEEE Information Theory Workshop, Jerusalem, Israel, pp 460–465Google Scholar
  24. 24.
    Shwartz-Ziv R, Tishby N (2017) Opening the black box of deep neural networks via information. CoRR, arXiv:abs/1703.00810
  25. 25.
    Ranzato M, Poultney C, Chopra S, LeCun Y (2006) Efficient learning of sparse representations with an energy-based model. In: Schölkopf B, Platt JC, Hoffman T (eds) Proceedings of the 19th international conference on neural information processing systems. MIT Press, Cambridge, MA, USA, pp 1137–1144Google Scholar
  26. 26.
    Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408MathSciNetzbMATHGoogle Scholar
  27. 27.
    Rifai S, Vincent P, Muller X, Glorot X, Bengio Y (2011) Contractive auto-encoders: explicit invariance during feature extraction. In: Getoor L, Scheffer T (eds) Proceedings of the 28th international conference on machine learning. Omnipress, USA, pp 833–840Google Scholar
  28. 28.
    Simard PY, LeCun YA, Denker JS, Victorri B (1998) Transformation invariance in pattern recognition–tangent distance and tangent propagation. Neural networks: tricks of the trade. Springer, New York, pp 239–274CrossRefGoogle Scholar
  29. 29.
    Alain G, Bengio Y (2014) What regularized auto-encoders learn from the data-generating distribution. J Mach Learn Res 15(1):3563–3593MathSciNetzbMATHGoogle Scholar
  30. 30.
    Sobel I (1990) An isotropic \(3\times 3\) image gradient operator. In: Freeman H (ed) Machine vision for three-dimensional scenes. Academic Press, London, pp 376–379Google Scholar
  31. 31.
    Kingma DP, Adam JB (2014) A method for stochastic optimization. arXiv:1412.6980
  32. 32.
    Chollet F et al (2015) Keras: The python deep learning library. https://keras.io
  33. 33.
    Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M et al (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv:1603.04467
  34. 34.
    Song X, Wu M, Jermaine C, Ranka S (2007) Conditional anomaly detection. IEEE Trans Knowl Data Eng 19(5):631–645CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adrian Alan Pol
    • 1
    • 2
  • Gianluca Cerminara
    • 2
  • Cecile Germain
    • 1
  • Maurizio Pierini
    • 2
  • Agrima Seth
    • 2
  1. 1.Université Paris-SaclayÎle-de-FranceFrance
  2. 2.CERNGenevaSwitzerland

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