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.
Similar content being viewed by others
References
The LHC Study Group (1995) The Large Hadron Collider, conceptual design. Technical report, CERN/AC/95-05 (LHC) Geneva
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):30–61
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):212
Chatrchyan S et al (2008) The CMS experiment at the CERN LHC. J Instrum Bristol 2006 Currens 3:S08004–1
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
De Guio F (2015) The data quality monitoring challenge at CMS: experience from first collisions and future plans. Technical report, CMS-CR-2015-329
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436
CMS collaboration (2010) Calibration of the CMS drift tube chambers and measurement of the drift velocity with cosmic rays. J Instrum 5(03):T03016
Tuura L, Eulisse G, Meyer A (2010) CMS data quality monitoring web service. J Phys Confer Ser (IOP Publishing) 219:072055
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
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
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
Liu FT, Ting KM, Zhou Z-H (2012) Isolation-based anomaly detection. ACM Trans Knowl Discov Data (TKDD) 6(1):3
Aggarwal CC (2015) Outlier analysis. Data mining. Springer, New York, pp 237–263
Aggarwal CC (2014) Data classification: algorithms and applications. CRC Press, Boca Raton
Cowan G, Cranmer K, Gross E, Vitells O (2011) Asymptotic formulae for likelihood-based tests of new physics. Eur Phys J C 71(2):1554
Bengio Y, LeCun Y (2007) Scaling learning algorithms towards ai. Large-Scale Kernel Mach 34(5):1–41
Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828
Goldstein M, Uchida S (2016) A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. PloS one 11(4):e0152173
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):363–387
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 1097–1105
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 1278–1286
Tishby N, Zaslavsky N (2015) Deep learning and the information bottleneck principle. In: Proceedings of IEEE Information Theory Workshop, Jerusalem, Israel, pp 460–465
Shwartz-Ziv R, Tishby N (2017) Opening the black box of deep neural networks via information. CoRR, arXiv:abs/1703.00810
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 1137–1144
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 11:3371–3408
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 833–840
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 239–274
Alain G, Bengio Y (2014) What regularized auto-encoders learn from the data-generating distribution. J Mach Learn Res 15(1):3563–3593
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 376–379
Kingma DP, Adam JB (2014) A method for stochastic optimization. arXiv:1412.6980
Chollet F et al (2015) Keras: The python deep learning library. https://keras.io
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
Song X, Wu M, Jermaine C, Ranka S (2007) Conditional anomaly detection. IEEE Trans Knowl Data Eng 19(5):631–645
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).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pol, A.A., Cerminara, G., Germain, C. et al. Detector Monitoring with Artificial Neural Networks at the CMS Experiment at the CERN Large Hadron Collider. Comput Softw Big Sci 3, 3 (2019). https://doi.org/10.1007/s41781-018-0020-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41781-018-0020-1