Artificial Intelligence Based Machine Learning Approach in High Energy Physics

In high energy physics experiments data quality plays a significant role for particle identification. Methods used in particle analysis are mainly based on high level knowledge and complex computation skills of human experts and require long time for data quality assurance. Artificial intelligence (AI) applications in various fields are getting important to improve the speed, accuracy and efficiency of human efforts. For this purpose, artificial intelligence-based machine learning approach can be used in particle physics analysis. Dielectrons (e-e+) are electromagnetic probes that provide information about evolution of the medium formed in high energy collisions due to lack of final state interactions. A high purity sample of e-e+ pairs can be obtained by traditional cut-based methods resulting in low efficiency. In this contribution, application of machine learning approaches in dielectron analysis is discussed.

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[1] Markert, C. (2005). What do we learn from resonance production in heavy ion collisions?. Journal of Physics G: Nuclear and Particle Physics, 31(4), S169.
[2] Torrieri, G., & Rafelski, J. (2001). Strange hadron resonances as a signature of freeze-out dynamics. Physics Letters B, 509(3-4), 239-245.
[3] Bleicher, M., & Aichelin, J. (2002). Strange resonance production: Probing chemical and thermal freeze-out in relativistic heavy ion collisions. Physics Letters B, 530(1-4), 81-87.
[4] Tawfik, A., & Shalaby, A. G. (2015). Balance function in high-energy collisions. Advances in High Energy Physics, 2015.
[5] Rapp, R., & Wambach, J. (2002). Chiral symmetry restoration and dileptons in relativistic heavy-ion collisions. Advances in Nuclear Physics, 1-205.
[6] Drell, S. D., & Yan, T. M. (1970). Massive lepton-pair production in hadron-hadron collisions at high energies. Physical Review Letters, 25(5), 316.
[7] Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE transactions on pattern analysis and machine intelligence, 20(8), 832-844.
[8] Trzcinski, T., Graczykowski, L. K. & Glinka, M. (2019). Using Random Forest Classifier for particle identification in the ALICE Experiment. Proceedings of Information Technology, Systems Research and Computational Physics, pp. 3–17.
[9] Liaw, A., & Wiener, M. (2002). Classification and regression by random Forest. R news, 2(3), 18-22.
[10] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
[11] Azhari, M., Alaoui, A., Achraoui, Z., Ettaki, B. & Zerouaoui, J. (2019). Adaptation of the Random Forest Method, Proceedings of the 4th International Conference on Smart City Applications, 1141–1146.
[12] Azhari, M., Alaoui, A., Abarda, A., Ettaki, B., & Zerouaoui, J. (2019). Using ensemble methods to solve the problem of pulsar search. In International Conference on Big Data and Networks Technologies (pp. 183-189). Springer, Cham.
[13] Azhari, M., Alaoui, A., Abarda, A., Ettaki, B., & Zerouaoui, J. (2019). A comparison of random forest methods for solving the problem of pulsar search. In The Proceedings of the Third International Conference on Smart City Applications (pp. 796-807). Springer, Cham.
[14] Mbaabu, O. (2020). Introduction to Random Forest in Machine Learning, Retrieved February 19, 2021, from https://www.section.io/engineering-education/introductionto-random-forest-in-machine-learning/.
[15] Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.".
[16] McCauley, T. (2014). CMS releases open data for Machine Learning, Retrieved October 15, 2014, from https://cms.cern/news/cms-releases-open-data-machinelearning
[17] Krintiras G. (2021). CMS Luminosity - Public Results, Retrieved January 10, 2021, from https://twiki.cern.ch/twiki/bin/view/CMSPublic/LumiPublic Results#2010_proton_proton_7_TeV_collisi
[18] CMS Collaboration, CMS luminosity information, for 2010 CMS open data, Retrieved November 22, 2017, from http://opendata.cern.ch/record/1050
[19] McCauley, T. (2014). J/psi to two electrons from 2010, CERN Open Data Portal.
[20] McCauley, T. (2014). Events with two electrons from 2010, CERN Open Data Portal.
[21] Python Software Foundation, The Python Language Reference, Retrieved February 5, 2021 from https://docs.python.org/3/reference/index.html.
[22] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikitlearn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830.
[23] Schwartz, M. D. (2021). Modern Machine Learning and Particle Physics. arXiv preprint arXiv:2103.12226.
[24] Chen, T., & He, T. (2015). Higgs boson discovery with boosted trees. In NIPS 2014 workshop on high-energy physics and machine learning (pp. 69-80). PMLR.
[25] Bourilkov, D., Acosta, D., Bortignon, P., Brinkerhoff, A., Carnes, A., Gleyzer, S., & Regnery, B. (2019). Machine Learning Techniques in the CMS Search for Higgs Decays to Dimuons. In EPJ Web of Conferences (Vol. 214, p. 06002). EDP Sciences.
[26] Arpaia, P., Azzopardi, G., Blanc, F., Bregliozzi, G., Buffat, X., Coyle, L., ... & Wenninger, J. (2021). Machine learning for beam dynamics studies at the CERN Large Hadron Collider. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 985, 164652.
[27] Trzcinski T. & Deja K. (2017) Assigning Quality Labels in the High-energy Physics Experiment Alice Using Machine Learning Algorithms, Proceedings of NICA days, 647-655.
[28] Nourbakhsh S. (2010). Studio degli eventi J/Ψ in due elettroni con i primi dati di CMS. (Doctoral dissertation, Sapienza University).