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 beenimplemented and tested within the MUonE experiment designed to search forNew Physics in the sector of anomalous magnetic moment of a muon. Theresults of the DNN based algorithm are comparable to the classical reconstruc-tion, 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 distributedsystems,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 tothe 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 newapproach for measuring the muon anomalous magnetic moment and electricdipole 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 Mag-netic 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 inphysics research section A: Accelerators, Spectrometers, Detectors and AssociatedEquipment, 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 momentmeasurement 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., Ri-ley 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 upgradedmuon beam at the SPS,Nuclear Instruments and Methods in Physics ResearchSection 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 ModelFitting with Applications to Image Analysis and Automated Cartography,Com-mun 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 poli-cies 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 Sys-tems, 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,Journalof 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 intelligentmonitoring 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 ComputerVision-Architectures, Datasets and Common Approaches. In:2022 InternationalJoint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE, 2022.
• [29] Kucharczyk M., Wolter M.: Track Finding with Deep Neural Networks,ComputerScience, 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 of2018 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 MachineLearning Research, vol. 12, pp. 2825–2830, 2011.
• [34] Peterson C.: Track finding with neural networks,Nuclear Instruments and Meth-ods in Physics Research Section A: Accelerators, Spectrometers, Detectors andAssociated 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 CREDOexperiment,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 graphneural 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 TrackReconstruction 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, Vol-ume 2: Special Sessions 18th International Conference, pp. 202–205, SpringerInternational Publishing, Cham, 2022.