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Title: Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Abstract

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $$\sqrt{s}$$ = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.

Authors:
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Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Univ. of Colorado, Boulder, CO (United States); Univ. of California, Santa Barbara, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
Contributing Org.:
The CMS collaboration
OSTI Identifier:
1637642
Alternate Identifier(s):
OSTI ID: 1637518; OSTI ID: 1777207
Report Number(s):
arXiv:2004.08262; FERMILAB-PUB-20-191-CMS; CMS-JME-18-002; CERN-EP-2020-037
Journal ID: ISSN 1748-0221; oai:inspirehep.net:1791648
Grant/Contract Number:  
AC02-07CH11359; SC0010005; SC0011702
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Instrumentation
Additional Journal Information:
Journal Volume: 15; Journal Issue: 06; Journal ID: ISSN 1748-0221
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; large detector-systems performance; pattern recognition; cluster finding; calibration and fitting methods

Citation Formats

Sirunyan, A. M., Tumasyan, A., Adam, W., Ambrogi, F., Bergauer, T., Dragicevic, M., Erö, J., Valle, A. Escalante Del, Flechl, M., Frühwirth, R., Jeitler, M., Krammer, N., Krätschmer, I., Liko, D., Madlener, T., Mikulec, I., Rad, N., Schieck, J., Schöfbeck, R., Spanring, M., Waltenberger, W., Wulz, C. -E., Zarucki, M., Drugakov, V., Mossolov, V., Gonzalez, J. Suarez, Darwish, M. R., Wolf, E. A. De, Croce, D. Di, Janssen, X., Lelek, A., Pieters, M., Sfar, H. Rejeb, Haevermaet, H. Van, Mechelen, P. Van, Putte, S. Van, Remortel, N. Van, Blekman, F., Bols, E. S., Chhibra, S. S., D'Hondt, J., Clercq, J. De, Lontkovskyi, D., Lowette, S., Marchesini, I., Moortgat, S., Python, Q., Skovpen, K., Tavernier, S., Doninck, W. Van, Mulders, P. Van, Beghin, D., Bilin, B., Clerbaux, B., Lentdecker, G. De, Delannoy, H., Dorney, B., Favart, L., Grebenyuk, A., Kalsi, A. K., Moureaux, L., Popov, A., Postiau, N., Starling, E., Thomas, L., Velde, C. Vander, Vanlaer, P., Vannerom, D., Cornelis, T., Dobur, D., Khvastunov, I., Niedziela, M., Roskas, C., Tytgat, M., Verbeke, W., Vermassen, B., Vit, M., Bondu, O., Bruno, G., Caputo, C., David, P., Delaere, C., Delcourt, M., Giammanco, A., Lemaitre, V., Prisciandaro, J., Saggio, A., Marono, M. Vidal, Vischia, P., Zobec, J., Alves, F. L., Alves, G. A., Silva, G. Correia, Hensel, C., Moraes, A., Teles, P. Rebello, Chagas, E. Belchior Batista Das, Carvalho, W., Chinellato, J., Coelho, E., Costa, E. M. Da, Silveira, G. G. Da, Damiao, D. De Jesus, Martins, C. De Oliveira, Souza, S. Fonseca De, Guativa, L. M. Huertas, Malbouisson, H., Martins, J., Figueiredo, D. Matos, Jaime, M. Medina, Almeida, M. Melo De, Herrera, C. Mora, Mundim, L., Nogima, H., Silva, W. L. Prado Da, Rosas, L. J. Sanchez, Santoro, A., Sznajder, A., Thiel, M., Manganote, E. J. Tonelli, Araujo, F. Torres Da Silva De, Pereira, A. Vilela, Bernardes, C. A., Calligaris, L., Tomei, T. R. Fernandez Perez, Gregores, E. M., Lemos, D. S., Mercadante, P. G., Novaes, S. F., Padula, SandraS., Aleksandrov, A., Antchev, G., Hadjiiska, R., Iaydjiev, P., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Bonchev, M., Dimitrov, A., Ivanov, T., Litov, L., Pavlov, B., Petkov, P., Petrov, A., Fang, W., Gao, X., Yuan, L., Ahmad, M., Hu, Z., Wang, Y., Chen, G. M., Chen, H. S., Chen, M., Jiang, C. H., Leggat, D., Liao, H., Liu, Z., Spiezia, A., Tao, J., Yazgan, E., Zhang, H., Zhang, S., Zhao, J., Agapitos, A., Ban, Y., Chen, G., Levin, A., Li, J., Li, L., Li, Q., Mao, Y., Qian, S. J., Wang, D., Wang, Q., Xiao, M., Avila, C., Cabrera, A., Florez, C., Hernández, C. F. González, Delgado, M. A. Segura, Guisao, J. Mejia, Alvarez, J. D. Ruiz, González, C. A. Salazar, Arbelaez, N. Vanegas, Giljanović, D., Godinovic, N., Lelas, D., Puljak, I., Sculac, T., Antunovic, Z., Kovac, M., Brigljevic, V., Ferencek, D., Kadija, K., Mesic, B., Roguljic, M., Starodumov, A., Susa, T., Ather, M. W., Attikis, A., Erodotou, E., Ioannou, A., Kolosova, M., Konstantinou, S., Mavromanolakis, G., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P. A., Rykaczewski, H., Tsiakkouri, D., Finger, M., Jr., M. Finger, Kveton, A., Tomsa, J., Ayala, E., Jarrin, E. Carrera, Abdalla, H., Khalil, S., Bhowmik, S., Oliveira, A. Carvalho Antunes De, Dewanjee, R. K., Ehataht, K., Kadastik, M., Raidal, M., Veelken, C., Eerola, P., Forthomme, L., Kirschenmann, H., Osterberg, K., Voutilainen, M., Garcia, F., Havukainen, J., Heikkilä, J. K., Karimäki, V., Kim, M. S., Kinnunen, R., Lampén, T., Lassila-Perini, K., Laurila, S., Lehti, S., Lindén, T., Luukka, P., Mäenpää, T., Siikonen, H., Tuominen, E., Tuominiemi, J., Tuuva, T., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Fabbro, B., Faure, J. L., Ferri, F., Ganjour, S., Givernaud, A., Gras, P., de Monchenault, G. Hamel, Jarry, P., Leloup, C., Lenzi, B., Locci, E., Malcles, J., Rander, J., Rosowsky, A., Sahin, M. 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Van De, Walsh, R., Wen, Y., Wichmann, K., Wissing, C., Zenaiev, O., Zlebcik, R., Aggleton, R., Bein, S., Benato, L., Benecke, A., Blobel, V., Dreyer, T., Ebrahimi, A., Feindt, F., Fröhlich, A., Garbers, C., Garutti, E., Gonzalez, D., Gunnellini, P., Haller, J., Hinzmann, A., Karavdina, A., Kasieczka, G., Klanner, R., Kogler, R., Kovalchuk, N., Kurz, S., Kutzner, V., Lange, J., Lange, T., Malara, A., Multhaup, J., Niemeyer, C. E. N., Perieanu, A., Reimers, A., Rieger, O., Scharf, C., Schleper, P., Schumann, S., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrück, G., Stober, F. M., Vormwald, B., Zoi, I., Akbiyik, M., Barth, C., Baselga, M., Baur, S., Berger, T., Butz, E., Caspart, R., Chwalek, T., Boer, W. De, Dierlamm, A., Morabit, K. El, Faltermann, N., Giffels, M., Goldenzweig, P., Gottmann, A., Harrendorf, M. A., Hartmann, F., Husemann, U., Kudella, S., Mitra, S., Mozer, M. U., Müller, D., Müller, Th., Musich, M., Nürnberg, A., Quast, G., Rabbertz, K., Schröder, M., Shvetsov, I., Simonis, H. J., Ulrich, R., Wassmer, M., Weber, M., Wöhrmann, C., Wolf, R., Wozniewski, S., Anagnostou, G., Asenov, P., Daskalakis, G., Geralis, T., Kyriakis, A., Loukas, D., Paspalaki, G., Diamantopoulou, M., Karathanasis, G., Kontaxakis, P., Manousakis-katsikakis, A., Panagiotou, A., Papavergou, I., Saoulidou, N., Stakia, A., Theofilatos, K., Vellidis, K., Vourliotis, E., Bakas, G., Kousouris, K., Papakrivopoulos, I., Tsipolitis, G., Evangelou, I., Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Mallios, S., Manitara, K., Manthos, N., Papadopoulos, I., Strologas, J., Triantis, F. A., Tsitsonis, D., Bartók, M., Chudasama, R., Csanad, M., Major, P., Mandal, K., Mehta, A., Nagy, M. I., Pasztor, G., Surányi, O., Veres, G. I., Bencze, G., Hajdu, C., Horvath, D., Sikler, F., Vámi, T. Á., Veszpremi, V., Vesztergombi, G., Beni, N., Czellar, S., Karancsi, J., Molnar, J., Szillasi, Z., Raics, P., Teyssier, D., Trocsanyi, Z. L., Ujvari, B., Csorgo, T., Metzger, W. J., Nemes, F., Novak, T., Choudhury, S., Komaragiri, J. R., Tiwari, P. C., Bahinipati, S., Kar, C., Kole, G., Mal, P., Bindhu, V. K. Muraleedharan Nair, Nayak, A., Sahoo, D. K., Swain, S. K., Bansal, S., Beri, S. B., Bhatnagar, V., Chauhan, S., Dhingra, N., Gupta, R., Kaur, A., Kaur, M., Kaur, S., Kumari, P., Lohan, M., Meena, M., Sandeep, K., Sharma, S., Singh, J. B., Virdi, A. K., Bhardwaj, A., Choudhary, B. C., Garg, R. B., Gola, M., Keshri, S., Kumar, Ashok, Naimuddin, M., Priyanka, P., Ranjan, K., Shah, Aashaq, Sharma, R., Bhardwaj, R., Bharti, M., Bhattacharya, R., Bhattacharya, S., Bhawandeep, U., Bhowmik, D., Dutta, S., Ghosh, S., Gomber, B., Maity, M., Mondal, K., Nandan, S., Purohit, A., Rout, P. K., Saha, G., Sarkar, S., Sarkar, T., Sharan, M., Singh, B., Thakur, S., Behera, P. K., Kalbhor, P., Muhammad, A., Pujahari, P. R., Sharma, A., Sikdar, A. K., Dutta, D., Jha, V., Kumar, V., Mishra, D. K., Netrakanti, P. K., Pant, L. M., Shukla, P., Aziz, T., Bhat, M. A., Dugad, S., Mohanty, G. B., Sur, N., Banerjee, S., Bhattacharya, S., Chatterjee, S., Das, P., Guchait, M., Karmakar, S., Kumar, S., Majumder, G., Mazumdar, K., Sahoo, N., Sawant, S., Dube, S., Kansal, B., Kapoor, A., Kothekar, K., Pandey, S., Rane, A., Rastogi, A., Sharma, S., Chenarani, S., Tadavani, E. Eskandari, Etesami, S. M., Khakzad, M., Najafabadi, M. Mohammadi, Naseri, M., Hosseinabadi, F. Rezaei, Felcini, M., Grunewald, M., Abbrescia, M., Aly, R., Calabria, C., Colaleo, A., Creanza, D., Cristella, L., Filippis, N. De, Palma, M. De, Florio, A. Di, Elmetenawee, W., Fiore, L., Gelmi, A., Iaselli, G., Ince, M., Lezki, S., Maggi, G., Maggi, M., Merlin, J. A., Miniello, G., My, S., Nuzzo, S., Pompili, A., Pugliese, G., Radogna, R., Ranieri, A., Selvaggi, G., Silvestris, L., Simone, F. 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@article{osti_1637642,
title = {Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques},
author = {Sirunyan, A. M. and Tumasyan, A. and Adam, W. and Ambrogi, F. and Bergauer, T. and Dragicevic, M. and Erö, J. and Valle, A. Escalante Del and Flechl, M. and Frühwirth, R. and Jeitler, M. and Krammer, N. and Krätschmer, I. and Liko, D. and Madlener, T. and Mikulec, I. and Rad, N. and Schieck, J. and Schöfbeck, R. and Spanring, M. and Waltenberger, W. and Wulz, C. -E. and Zarucki, M. and Drugakov, V. and Mossolov, V. and Gonzalez, J. Suarez and Darwish, M. R. and Wolf, E. A. De and Croce, D. Di and Janssen, X. and Lelek, A. and Pieters, M. and Sfar, H. Rejeb and Haevermaet, H. Van and Mechelen, P. Van and Putte, S. Van and Remortel, N. Van and Blekman, F. and Bols, E. S. and Chhibra, S. S. and D'Hondt, J. and Clercq, J. De and Lontkovskyi, D. and Lowette, S. and Marchesini, I. and Moortgat, S. and Python, Q. and Skovpen, K. and Tavernier, S. and Doninck, W. Van and Mulders, P. Van and Beghin, D. and Bilin, B. and Clerbaux, B. and Lentdecker, G. 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J. and Jabeen, S. and Jeng, G. Y. and Kellogg, R. G. and Mignerey, A. C. and Nabili, S. and Ricci-Tam, F. and Seidel, M. and Shin, Y. H. and Skuja, A. and Tonwar, S. C. and Wong, K. and Abercrombie, D. and Allen, B. and Bi, R. and Brandt, S. and Busza, W. and Cali, I. A. and D'Alfonso, M. and Ceballos, G. Gomez and Goncharov, M. and Harris, P. and Hsu, D. and Hu, M. and Klute, M. and Kovalskyi, D. and Lee, Y. -J. and Luckey, P. D. and Maier, B. and Marini, A. C. and Mcginn, C. and Mironov, C. and Narayanan, S. and Niu, X. and Paus, C. and Rankin, D. and Roland, C. and Roland, G. and Shi, Z. and Stephans, G. S. F. and Sumorok, K. and Tatar, K. and Velicanu, D. and Wang, J. and Wang, T. W. and Wyslouch, B. and Chatterjee, R. M. and Evans, A. and Guts, S. and Hansen, P. and Hiltbrand, J. and Jain, Sh. and Kubota, Y. and Lesko, Z. and Mans, J. and Revering, M. and Rusack, R. and Saradhy, R. and Schroeder, N. and Wadud, M. A. and Acosta, J. G. and Oliveros, S. and Bloom, K. and Chauhan, S. and Claes, D. R. and Fangmeier, C. and Finco, L. and Golf, F.},
abstractNote = {Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $\sqrt{s}$ = 13TeV, corresponding to an integrated luminosity of 35.9 fb-1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.},
doi = {10.1088/1748-0221/15/06/P06005},
journal = {Journal of Instrumentation},
number = 06,
volume = 15,
place = {United States},
year = {2020},
month = {6}
}

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