Machine learning based event reconstruction for the MUonE experiment

• Autorzy: Zdybał Miłosz , Kucharczyk Marcin , Wolter Marcin

Identyfikatory

DOI

10.7494/csci.2024.25.1.5690

• Języki publikacji: EN

Abstrakty

EN

A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase.The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies.

Słowa kluczowe

EN

machine learning; artificial neural networks; track reconstruction; high energy physics

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2024
• Tom: T. 25 (1)
• Strony: 147--167
• Opis fizyczny: Bibliogr. 40 poz., rys., wykr.

Twórcy

autor

Zdybał Miłosz

• The Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences,https://www.ifj.edu.pl

autor

Kucharczyk Marcin

• The Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences,https://www.ifj.edu.pl

autor

Wolter Marcin

• The Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, https://www.ifj.edu.pl

Bibliografia

• [1] Abadi M., Agarwal A., Barham P., Brevdo E., Chen Z., Citro C., Corrado G.S.,et al.: TensorFlow: Large-scale machine learning on heterogeneous distributed systems, arXiv preprint arXiv:160304467, 2016.
• [2] Abbiendi G.: Letter of Intent: the MUonE project, CERN-SPSC-2019-026,SPSC-I-252, 2019. https://cds.cern.ch/record/2677471.
• [3] Abbiendi G., Ballerini G., Banerjee D., Bernhard J., Bonanomi M., Brizzolari C.,Foggetta L.,et al.: A study of muon-electron elastic scattering in a test beam, Journal of Instrumentation, vol. 16(06), P06005, 2021. doi: 10.1088/1748-0221/16/06/p06005.
• [4] Abbiendi G., Carloni Calame C.M., Marconi U., Matteuzzi C., Montagna G.,Nicrosini O., Passera M.,et al.: Measuring the leading hadronic contribution to the muon g-2 viaμescattering, The European Physical Journal C, vol. 77(3), 2017. doi: 10.1140/epjc/s10052-017-4633-z.
• [5] Abe M., Bae S., Beer G., Bunce G., Choi H., Choi S., Chung M., et al.: A new approach for measuring the muon anomalous magnetic moment and electric dipole moment, Progress of Theoretical and Experimental Physics, vol. 2019(5), 053C02, 2019.
• [6] Abi B., Albahri T., Al-Kilani S., Allspach D., Alonzi L.P., Anastasi A.,Anisenkov A.,et al.: Measurement of the Positive Muon Anomalous Magnetic Moment to 0.46 ppm, Physical Review Letter, vol. 126, 141801, 2021.doi: 10.1103/PhysRevLett.126.141801.
• [7] Agostinelli S., Allison J., Amako K., Apostolakis J., Araujo H., Arce P., Asai M., et al.: GEANT4–a simulation toolkit, Nuclear instruments and methods in physics research section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 506(3), pp. 250–303, 2003. doi: 10.1016/S0168-9002(03)01368-8.
• [8] Bennett G.W., Bousquet B., Brown H.N., Bunce G., Carey R.M., Cushman P.,Danby G.T.,et al.: Final report of the E821 muon anomalous magnetic moment measurement at BNL, Physical Review D, vol. 73(7), 072003, 2006.
• [9] Bhattacharya S., Chernyavskaya N., Ghosh S., Gray L., Kieseler J., Klijnsma T.,Long K.,et al.: GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter, 2022. doi: 10.48550/ARXIV.2203.01189.
• [10] Brun R., Rademakers F.: ROOT – An object oriented data analysis framework, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 389(1), pp. 81–86, 1997. doi: 10.1016/S0168-9002(97)00048-X.
• [11] Caillou S., Calafiura P., Farrell S.A., Ju X., Murnane D.T., Rougier C., Stark J.,Vallier A.: ATLAS ITk Track Reconstruction with a GNN-based pipeline. Technical report: ATL-COM-ITK-2022-057, 2022. https://cds.cern.ch/record/2815578/.
• [12] Cerati G., Elmer P., Krutelyov S., Lantz S., Lefebvre M., McDermott K., Riley D., et al.: Kalman filter tracking on parallel architectures, Journal of Physics: Conference Series, vol. 898, 042051, 2017. doi: 10.1088/1742-6596/898/4/042051.
• [13] Chollet F.: Deep learning with Python, Simon and Schuster, 2021.
• [14] Choma N., Murnane D., Ju X., Calafiura P., Conlon S., Farrell S., Cerati G., et al.: Track seeding and labelling with embedded-space graph neural networks, arXiv preprint arXiv:200700149, 2020.
• [15] Doble N., Gatignon L., Holtey von G., Novoskoltsev F.N.: The upgraded muon beam at the SPS, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 343(2–3), pp. 351–362, 1993. doi: 10.1016/0168-9002(94)90212-7.
• [16] Farrell S., Anderson D., Calafiura P., Cerati G., Gray L., Kowalkowski J., Mudigonda M., et al.: The HEP. TrkX Project: deep neural networks for HL-LHConline and offline tracking, EPJ Web of Conferences, vol. 150, 00003, 2017.doi: 10.1051/epjconf/201715000003.
• [17] Farrell S., Calafiura P., Mudigonda M., Mr. Prabhat, Anderson D., Bendavi J.,Spiropoulou M., et al.: Particle Track Reconstruction with Deep Learning. In: 31st Annual Conference on Neural Information Processing Systems (NIPS), 2017. https://dl4physicalsciences.github.io/files/nipsdlps201728.pdf.
• [18] Farrell S., Calafiura P., Mudigonda M., Prabhat, Anderson D., Vlimant J.R.,Zheng S.,et al.: Novel deep learning methods for track reconstruction, 2018. doi: 10.48550/ARXIV.1810.06111.
• [19] Fischler M.A., Bolles R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Commun ACM, vol. 24(6), pp. 381–395, 1981. doi: 10.1145/358669.358692.
• [20] Funika W., Koperek P., Kitowski J.: Continuous self-adaptation of control policies in automatic cloud management, Concurrency and Computation: Practiceand Experience, vol. 35(20), e7371, 2023.
• [21] Hochreiter S., Schmidhuber J.: Long short-term memory, Neural Computation, vol. 9(8), pp. 1735–1780, 1997.
• [22] Ju X., Farrell S., Calafiura P., Murnane D., Prabhat, Gray L., Klijnsma T., et al.: Graph Neural Networks for Particle Reconstruction in High Energy Physicsdetectors. In: 33rd Annual Conference on Neural Information Processing Systems, 2020.
• [23] Ju X., Murnane D., Calafiura P., Choma N., Conlon S., Farrell S., Xu Y., et al.: Performance of a geometric deep learning pipeline for HL-LHC particle tracking, The European Physical Journal C, vol. 81, 876, 2021.
• [24] Kalman R.E.: A new approach to linear filtering and prediction problems, Journal of Basic Engineering, vol. 82(1), pp. 35–45, 1960.
• [25] Keras, https://keras.io, 2015.
• [26] Kopciewicz P., Akiba K.C., Szumlak T., Sitko S., Barter W., Buytaert J., Ek-lund L.,et al.: Simulation and optimization studies of the LHCb beetle readoutASIC and machine learning approach for pulse shape reconstruction, Sensors, vol. 21(18), 6075, 2021.
• [27] Kopciewicz P., Szumlak T., Majewski M., Akiba K., Augusto O., Back J.,Bobulska D.S., et al.: The upgrade I of LHCb VELO – towards an intelligent monitoring platform, Journal of Instrumentation, vol. 15(06), C06009, 2020. doi: 10.1088/1748-0221/15/06/C06009.
• [28] Krzywda M., Lukasik S., Gandomi A.H.: Graph Neural Networks in Computer Vision-Architectures, Data sets and Common Approaches. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE, 2022.
• [29] Kucharczyk M., Wolter M.: Track Finding with Deep Neural Networks, Computer Science, vol. 20(4), 2019. doi: 10.7494/csci.2019.20.4.3376.
• [30] LeCun Y., Bengio Y., Hinton G.: Deep Learning, Nature, vol. 521, pp. 436–444, 2015. doi: 10.1038/nature14539.
• [31] LHCb Experiment, LHCb Collaboration: Event collected at the beginning of 2018 data taking, 2018. http://cds.cern.ch/record/2315673.
• [32] Paszke A., Gross S., Massa F., Lerer A., Bradbury J., Chanan G., et al.: PyTorch:An Imperative Style, High-Performance Deep Learning Library, 2019.
• [33] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O.,Blondel M., et al.: Scikit-learn: Machine Learning in Python, Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
• [34] Peterson C.: Track finding with neural networks, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 279(3), pp. 537–545, 1989.
• [35] Piekarczyk M., Bar O., Bibrzycki L., Nied ́zwiecki M., Rzecki K., Stuglik S.,Andersen T., et al.: CNN-based classifier as an offline trigger for the CREDO experiment, Sensors, vol. 21(14), 4804, 2021. doi: 10.3390/s21144804.
• [36] PyTorch: MSELoss – PyTorch 1.11.0 documentation. https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html.
• [37] Salzburger A.: The ATLAS Track Extrapolation Package, Technical report ATL-SOFT-PUB-2007-005; ATL-COM-SOFT-2007-010, 2007. https://cds.cern.ch/record/1038100.
• [38] Scarselli F., Gori M., Tsoi A.C., Hagenbuchner M., Monfardini G.: The graph neural network model, IEEE Transactions on Neural Networks, vol. 20(1), pp. 61–80, 2008.
• [39] Vinyals O., Toshev A., Bengio S., Erhan D.: Show and tell: A neural imagecaption generator. In: Proceedings of the IEEE Conference on Computer Visionand Pattern Recognition, pp. 3156–3164, 2015.
• [40] Zdyba l M., Kucharczyk M., Wolter M.: DNN Based Prototype of the Track Reconstruction Algorithm for the MUonE Experiment. In: S.R. Gonz ́alez,J.M. Machado, A. Gonz ́alez-Briones, J. Wikarek, R. Loukanova, G. Katranas,R. Casado-Vara (eds.), Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference, pp. 202–205, Springer International Publishing, Cham, 2022.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).