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9. Referencias

1

Lyndon Evans and Philip Bryant. LHC machine. Journal of Instrumentation, 3(08):S08001–S08001, aug 2008. URL: https://doi.org/10.1088/1748-0221/3/08/s08001, doi:10.1088/1748-0221/3/08/s08001.

2

Lydia Audrey Beresford. Searches for Dijet Resonances using $\sqrt s = 13$ TeV proton–proton collision data recorded by the ATLAS detector at the Large Hadron Collider. Dijet Resognance Searches with the ATLAS detector at 13 TeV and jet punch-through corrections. PhD thesis, University of Oxford, 2017. URL: https://cds.cern.ch/record/2642397, doi:10.1007/978-3-319-97520-7.

3

Guido Altarelli. The standard model of particle physics. 2005. arXiv:hep-ph/0510281.

4

T. Morii, C. S. Lim, and S. N. Mukherjee. The physics of the Standard Model and Beyond. World Scientific, 2004.

5

Wikimedia Commons contributors. File:modelo estándar.svg. 2022. URL: https://commons.wikimedia.org/w/index.php?title=File:Modelo_Est%C3%A1ndar.svg&oldid=627837863.

6

Reina Coromoto Camacho Toro. Search of new resonances decaying into top quark pairs with the ATLAS detector at the LHC and jet calibration studies. Theses, Université Blaise Pascal - Clermont-Ferrand II, July 2012. URL: https://tel.archives-ouvertes.fr/tel-00818796.

7

C. P. Burgess and Guy D. Moore. The standard model: general features. Cambridge Univ. Press, 2013.

8

G. Aad and others. The ATLAS Experiment at the CERN Large Hadron Collider. JINST, 3:S08003, 2008. doi:10.1088/1748-0221/3/08/S08003.

9

S. Chatrchyan and others. The CMS Experiment at the CERN LHC. JINST, 3:S08004, 2008. doi:10.1088/1748-0221/3/08/S08004.

10

Aad et al. Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc. Physics Letters B, 716(1):1–29, 2012. URL: https://www.sciencedirect.com/science/article/pii/S037026931200857X, doi:https://doi.org/10.1016/j.physletb.2012.08.020.

11

Chatrchyan et al. Observation of a new boson at a mass of 125 gev with the cms experiment at the lhc. Physics Letters B, 716(1):30–61, 2012. URL: https://www.sciencedirect.com/science/article/pii/S0370269312008581, doi:https://doi.org/10.1016/j.physletb.2012.08.021.

12

Aad et al. Combined Measurement of the Higgs Boson Mass in $pp$ Collisions at $\sqrt s=7$ and 8 TeV with the ATLAS and CMS Experiments. Physical Review Letters, 114:191803, May 2015. URL: https://link.aps.org/doi/10.1103/PhysRevLett.114.191803, doi:10.1103/PhysRevLett.114.191803.

13

Peter W. Higgs. Broken symmetries and the masses of gauge bosons. Physical Review Letters, 13:508–509, Oct 1964. URL: https://link.aps.org/doi/10.1103/PhysRevLett.13.508, doi:10.1103/PhysRevLett.13.508.

14

F. Englert and R. Brout. Broken symmetry and the mass of gauge vector mesons. Physical Review Letters, 13:321–323, Aug 1964. URL: https://link.aps.org/doi/10.1103/PhysRevLett.13.321, doi:10.1103/PhysRevLett.13.321.

15

C Sutton. Quantum Chromodynamics. Oct 2016. [Online; accessed 24. Mar. 2022]. URL: https://www.britannica.com/science/quantum-chromodynamics.

16

David Griffiths. Feynmann Rules for Chromodynamics. John Wiley & Sons, Inc., 1987.

17

The Nobel Prize. The Nobel Prize in Physics 2004. URL: https://www.nobelprize.org/prizes/physics/2004/summary/ (visited on 2022-03-25).

18

Bethke, Siegfried, Dissertori, Günther, and Salam, Gavin. World summary of $\alpha _s$(2015). EPJ Web of Conferences, 120:07005, 2016. URL: https://doi.org/10.1051/epjconf/201612007005, doi:10.1051/epjconf/201612007005.

19

M Mangano. QCD and the physics of Hadronic Collisions. CERN Yellow Reports: School Proceedings, 4:27–62. 36 p, 2018. URL: http://cds.cern.ch/record/2674114, doi:10.23730/CYRSP-2018-004.27.

20

Zachary L Marshall. A Measurement of Jet Shapes in Proton-Proton Collisions at 7.0 TeV Center-of-Mass Energy with the ATLAS Detector at the Large Hadron Collider. PhD thesis, California Institute of Technology, 2010. Presented on 09 Nov 2010. URL: https://cds.cern.ch/record/1308447.

21

ROBERT M. HARRIS and KONSTANTINOS KOUSOURIS. SEARCHES FOR DIJET RESONANCES AT HADRON COLLIDERS. International Journal of Modern Physics A, 26(30n31):5005–5055, dec 2011. URL: https://doi.org/10.1142%2Fs0217751x11054905, doi:10.1142/s0217751x11054905.

22

Deepak Kar. Theoretical view of collisions and simulating them. In Experimental Particle Physics, 2053-2563, pages 4–1 to 4–40. IOP Publishing, 2019. URL: https://dx.doi.org/10.1088/2053-2563/ab1be6ch4, doi:10.1088/2053-2563/ab1be6ch4.

23

C. P. Burgess and Guy D. Moore. Hadronic Interactions. Cambridge Univ. Press, 2013.

24

Tomio Kobayashi. Experimental verification of the standard model of particle physics. Proceedings of the Japan Academy, Series B, 97:211–235, 05 2021. doi:10.2183/pjab.97.013.

25

National Research Council. The Past 25 Years: Establishing the Standard Model. The National Academies Press, 1998. URL: https://nap.nationalacademies.org/catalog/6045/elementary-particle-physics-revealing-the-secrets-of-energy-and-matter, doi:10.17226/6045.

26

Matthew Philip Mccullough. Beyond the Standard Model. CERN Summer School Lectures. 2019. Accessed 01/04/22. URL: https://summerstudent.web.cern.ch/lectures-2019/beyond-standard-model.

27

Andre de Gouvea. Neutrino masses and mixing - theory. 2009. URL: https://arxiv.org/abs/0902.4656, doi:10.48550/ARXIV.0902.4656.

28

John Ellis. Outstanding questions: physics beyond the standard model. Philos. Trans. A Math. Phys. Eng. Sci., 370(1961):818–830, feb 2012.

29

Michael Paul Gough. Beyond lcdm: bits join the dark side. 2016. arXiv:arXiv:1607.00330.

30

Michael Creutz. Cp violation in qcd. 2018. URL: https://arxiv.org/abs/1810.03543, doi:10.48550/ARXIV.1810.03543.

31

Thomas Mannel. Theory and phenomenology of CP violation. Nuclear Physics B - Proceedings Supplements, 167:115–119, 2007. doi:10.1016/j.nuclphysbps.2006.12.083.

32

Emily Smith. The hierarchy problem. 2019. Accessed 02/04/22. URL: https://homes.psd.uchicago.edu/~sethi/Teaching/P445-S2019/Emily_Smith_QFT_III_Final_Paper.pdf.

33

Glenn Elert. The physics hypertextbook: beyond the standard model. 1998. Accessed 02/04/2022. URL: https://physics.info/beyond/.

34

T S Virdee. Beyond the standard model of particle physics. Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences, 374(2075):20150259, August 2016.

35

York University. Beyond the standard model. Accessed 02/04/2022. URL: https://hep.info.yorku.ca/beyond-the-standard-model/.

36

TH. KALUZA. On the unification problem in physics. International Journal of Modern Physics D, 27(14):1870001, oct 2018. URL: https://doi.org/10.1142%2Fs0218271818700017, doi:10.1142/s0218271818700017.

37

Oskar Klein. Quantum Theory and Five-Dimensional Theory of Relativity. (In German and English). Zeitschrift für Physik A Hadrons and nuclei, 37:895–906, 1926. doi:10.1007/BF01397481.

38

Abdel Pérez-Lorenzana. An introduction to extra dimensions. Journal of Physics: Conference Series, 18:224–269, jan 2005. URL: https://doi.org/10.1088/1742-6596/18/1/006, doi:10.1088/1742-6596/18/1/006.

39

Kevin Wray. An introduction to string theory. 2011. Accessed 03/04/2022. URL: https://math.berkeley.edu/~kwray/papers/string_theory.pdf.

40

ATLAS Collaboration. SUSY March 2022 Summary Plot Update. 2022. URL: https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATL-PHYS-PUB-2022-013/.

41

Jeong Han Kim, Kyoungchul Kong, Benjamin Nachman, and Daniel Whiteson. The motivation and status of two-body resonance decays after the LHC run 2 and beyond. Journal of High Energy Physics, apr 2020. URL: https://doi.org/10.1007%2Fjhep04%282020%29030, doi:10.1007/jhep04(2020)030.

42

Robert M. Harris. Search for new particles decaying to dijets, bb\=, and tt\= at CDF. In AIP Conference Proceedings. AIP, 1996. URL: https://doi.org/10.1063%2F1.49662, doi:10.1063/1.49662.

43

A. M. Sirunyan et al. Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at \textdollar \textdollar \sqrt$\lbrace $s$\rbrace $ \textdollar \textdollar = 13 TeV. Journal of High Energy Physics, may 2020. URL: https://doi.org/10.1007%2Fjhep05%282020%29033, doi:10.1007/jhep05(2020)033.

44

M. Aaboud et al. Search for New Phenomena in Dijet Events using 139 fb$^−1$ of $pp$ collisions at $\sqrt s$ = 13TeV collected with the ATLAS Detector. Physical Review D, sep 2017. URL: https://doi.org/10.1103%2Fphysrevd.96.052004, doi:10.1103/physrevd.96.052004.

45

G. Aad et al. Search for New Phenomena in Dijet Mass and Angular Distributions with the ATLAS Detector at √s = 13 TeV. Physics Letters B, 754:302–322, mar 2016. URL: https://doi.org/10.1016%2Fj.physletb.2016.01.032, doi:10.1016/j.physletb.2016.01.032.

46

Rebeca Gonzalez Suarez and Darren Price. ATLAS Control Room: LHC Run 3 Restart at 13.6 TeV. General Photo, Jul 2022. URL: http://cds.cern.ch/record/2814929.

47

Paul Langacker. The Physics of Heavy Z' Gauge Bosons. Reviews of Modern Physics, 81(3):1199–1228, aug 2009. URL: https://doi.org/10.1103%2Frevmodphys.81.1199, doi:10.1103/revmodphys.81.1199.

48

Deepak Kar. Analysis objects. In Experimental Particle Physics, 2053-2563, pages 3–1 to 3–32. IOP Publishing, 2019. URL: https://dx.doi.org/10.1088/2053-2563/ab1be6ch3, doi:10.1088/2053-2563/ab1be6ch3.

49

Elsa Fabiola Vázquez Valencia. Introducción a los Detectores de Partículas. 2017. Accessed: 26-07-2022. URL: https://indico.cern.ch/event/577111/contributions/2662902/attachments/1502285/2340054/Detectores_MexicanTeacherProgramme.pdf.

50

missing note in Pequenao:1505342

51

Thomas Hebbeker and Kerstin Hoepfner. Muon Spectrometers, pages 473–496. Springer Berlin Heidelberg, Berlin, Heidelberg, 2012. URL: https://doi.org/10.1007/978-3-642-13271-1_19, doi:10.1007/978-3-642-13271-1_19.

52

John E. Huth and others. Toward a standardization of jet definitions. In 1990 DPF Summer Study on High-energy Physics: Research Directions for the Decade (Snowmass 90), 0134–136. 12 1990.

53

Ryan Atkin. Review of jet reconstruction algorithms. Journal of Physics: Conference Series, 645:012008, oct 2015. URL: https://doi.org/10.1088/1742-6596/645/1/012008, doi:10.1088/1742-6596/645/1/012008.

54

Philipp Schieferdecker. Jet algorithms. 4 2009. URL: https://twiki.cern.ch/twiki/pub/Sandbox/Lecture/Philipp_Schieferdeckers_Lecture.pdf.

55

Gavin P Salam and Grégory Soyez. A practical seedless infrared-safe cone jet algorithm. Journal of High Energy Physics, 2007(05):086–086, may 2007. URL: https://doi.org/10.1088%2F1126-6708%2F2007%2F05%2F086, doi:10.1088/1126-6708/2007/05/086.

56

Stephen D. Ellis and Davison E. Soper. Successive combination jet algorithm for hadron collisions. Physical Review D, 48:3160–3166, Oct 1993. URL: https://link.aps.org/doi/10.1103/PhysRevD.48.3160, doi:10.1103/PhysRevD.48.3160.

57

Matteo Cacciari, Gavin P Salam, and Gregory Soyez. The anti-ktjet clustering algorithm. Journal of High Energy Physics, 2008(04):063–063, apr 2008. URL: https://doi.org/10.1088/1126-6708/2008/04/063, doi:10.1088/1126-6708/2008/04/063.

58

Yu.L Dokshitzer, G.D Leder, S Moretti, and B.R Webber. Better jet clustering algorithms. Journal of High Energy Physics, 1997(08):001–001, aug 1997. URL: https://doi.org/10.1088/1126-6708/1997/08/001, doi:10.1088/1126-6708/1997/08/001.

59

Matteo Cacciari, Gavin P. Salam, and Gregory Soyez. FastJet user manual. The European Physical Journal C, mar 2012. URL: https://doi.org/10.1140%2Fepjc%2Fs10052-012-1896-2, doi:10.1140/epjc/s10052-012-1896-2.

60

Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant, Olmo Cerri, Harvey B. Newman, Avikar Periwal, Maria Spiropulu, Javier M. Duarte, and Maurizio Pierini. Interaction networks for the identification of boosted $h\ensuremath \rightarrow b\overline b$ decays. Phys. Rev. D, 102:012010, Jul 2020. URL: https://link.aps.org/doi/10.1103/PhysRevD.102.012010, doi:10.1103/PhysRevD.102.012010.

61

Deepak Kar. Advanced topic: jet substructure. In Experimental Particle Physics, 2053-2563, pages 8–1 to 8–28. IOP Publishing, 2019. URL: https://dx.doi.org/10.1088/2053-2563/ab1be6ch8, doi:10.1088/2053-2563/ab1be6ch8.

62

Deepak Kar. Analysis. In Experimental Particle Physics, 2053-2563, pages 5–1 to 5–51. IOP Publishing, 2019. URL: https://dx.doi.org/10.1088/2053-2563/ab1be6ch5, doi:10.1088/2053-2563/ab1be6ch5.

63

Dimitri Bourilkov. Machine and deep learning applications in particle physics. International Journal of Modern Physics A, 34(35):1930019, dec 2019. URL: https://doi.org/10.1142%2Fs0217751x19300199, doi:10.1142/s0217751x19300199.

64

Dan Guest, Kyle Cranmer, and Daniel Whiteson. Deep learning and its application to LHC physics. Annual Review of Nuclear and Particle Science, 68(1):161–181, oct 2018. URL: https://doi.org/10.1146%2Fannurev-nucl-101917-021019, doi:10.1146/annurev-nucl-101917-021019.

65

Pushpalatha C. Bhat, Harrison B. Prosper, and Scott S. Snyder. Bayesian analysis of multisource data. Physics Letters B, 407:73–78, 1997. doi:10.1016/S0370-2693(97)00723-5.

66

Fabricio Jimenez. Model independent searches for New Physics using Machine Learning at the ATLAS experiment. Theses, Université Clermont Auvergne [2017-2020], September 2019. URL: https://tel.archives-ouvertes.fr/tel-02402488.

67

HEP ML Community. A Living Review of Machine Learning for Particle Physics. URL: https://iml-wg.github.io/HEPML-LivingReview/.

68

Pankaj Mehta, Marin Bukov, Ching-Hao Wang, Alexandre G.R. Day, Clint Richardson, Charles K. Fisher, and David J. Schwab. A high-bias, low-variance introduction to machine learning for physicists. Physics Reports, 810:1–124, may 2019. URL: https://doi.org/10.1016%2Fj.physrep.2019.03.001, doi:10.1016/j.physrep.2019.03.001.

69

Karagiorgi et al. Machine learning in the search for new fundamental physics. 2021. URL: https://arxiv.org/abs/2112.03769, doi:10.48550/ARXIV.2112.03769.

70

Gilles Louppe. Understanding random forests: from theory to practice. 2015. arXiv:1407.7502.

71

Robert E. Schapire. The Boosting Approach to Machine Learning: An Overview, pages 149–171. Springer New York, New York, NY, 2003. URL: https://doi.org/10.1007/978-0-387-21579-2_9, doi:10.1007/978-0-387-21579-2_9.

72

Katherine Fraser, Samuel Homiller, Rashmish K. Mishra, Bryan Ostdiek, and Matthew D. Schwartz. Challenges for unsupervised anomaly detection in particle physics. Journal of High Energy Physics, mar 2022. URL: https://doi.org/10.1007%2Fjhep03%282022%29066, doi:10.1007/jhep03(2022)066.

73

Andrea De Simone and Thomas Jacques. Guiding new physics searches with unsupervised learning. The European Physical Journal C, mar 2019. URL: https://doi.org/10.1140%2Fepjc%2Fs10052-019-6787-3, doi:10.1140/epjc/s10052-019-6787-3.

74

Cern - spotlight: the lhc olympics 2006. 2006. URL: https://public-archive.web.cern.ch/en/Spotlight/SpotlightOlympics-en.html.

75

Gregor Kasieczka et al. The LHC olympics 2020 a community challenge for anomaly detection in high energy physics. Reports on Progress in Physics, 84(12):124201, dec 2021. URL: https://doi.org/10.1088%2F1361-6633%2Fac36b9, doi:10.1088/1361-6633/ac36b9.

76

LHC Olympics 2020. Welcome to the home of the lhc olympics 2020! 2020. URL: https://lhco2020.github.io/homepage/.

77

Robert K Merton and Norman William Storer. The sociology of science. University of Chicago Press, 1973.

78

Karen White. Publications output: u.s. trends and international comparisons. 2021. URL: https://ncses.nsf.gov/pubs/nsb20214/.

79

National Academies of Sciences, Engineering and Medicine. Reproducibility and Replicability in Science. The National Academies Press, Washington, DC, 2019. ISBN 978-0-309-48616-3. URL: https://nap.nationalacademies.org/catalog/25303/reproducibility-and-replicability-in-science, doi:10.17226/25303.

80

Monya Baker. 1,500 scientists lift the lid on reproducibility. Nature, 533(7604):452–454, May 2016. URL: https://doi.org/10.1038/533452a, doi:10.1038/533452a.

81

Nature. Reality check on reproducibility. Nature, 533(7604):437–437, May 2016. URL: https://doi.org/10.1038/533437a, doi:10.1038/533437a.

82

The Turing Way Community. The Turing Way: A handbook for reproducible, ethical and collaborative research. November 2021. This work was supported by The UKRI Strategic Priorities Fund under the EPSRC Grant EP/T001569/1, particularly the "Tools, Practices and Systems" theme within that grant, and by The Alan Turing Institute under the EPSRC grant EP/N510129/1. URL: https://doi.org/10.5281/zenodo.5671094, doi:10.5281/zenodo.5671094.

83

Scott Chacon and Ben Straub. Pro git: Everything you need to know about Git. Apress, second edition, 2014. URL: https://git-scm.com/book/en/v2.

84

Choose an open source license. Accessed: 14-04-2022. URL: https://choosealicense.com.

85

GNU Operating System. Gnu general public license. Accessed: 14-04-2022. URL: https://www.gnu.org/licenses/gpl-3.0.en.html.

86

Creative Commons. Atribución 4.0 internacional (cc by 4.0). Accessed: 14-04-2022. URL: https://creativecommons.org/licenses/by/4.0/deed.es.

87

Nature Human Behaviour. The importance of no evidence. Nature Human Behaviour, 3(3):197–197, Mar 2019. URL: https://doi.org/10.1038/s41562-019-0569-7, doi:10.1038/s41562-019-0569-7.

88

Torbjörn Sjöstrand et al. An introduction to PYTHIA 8.2. Computer Physics Communications, 191:159–177, jun 2015. URL: https://doi.org/10.1016%2Fj.cpc.2015.01.024, doi:10.1016/j.cpc.2015.01.024.

89

Manuel Bähr et al. Herwig$\mathplus $$\mathplus $ physics and manual. The European Physical Journal C, 58(4):639–707, nov 2008. URL: https://doi.org/10.1140%2Fepjc%2Fs10052-008-0798-9, doi:10.1140/epjc/s10052-008-0798-9.

90

J. de Favereau, and C. Delaere, P. Demin, A. Giammanco, V. Lema\^ıtre, A. Mertens, and M. Selvaggi. DELPHES 3: a modular framework for fast simulation of a generic collider experiment. Journal of High Energy Physics, feb 2014. URL: https://doi.org/10.1007%2Fjhep02%282014%29057, doi:10.1007/jhep02(2014)057.

91

Gregor Kasieczka, Ben Nachman, and David Shih. R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge. April 2019. URL: https://doi.org/10.5281/zenodo.2629073, doi:10.5281/zenodo.2629073.

92

Gregor Kasieczka, Benjamin Nachman, and David Shih. Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge. November 2019. URL: https://doi.org/10.5281/zenodo.4536624, doi:10.5281/zenodo.4536624.

93

Anthony J. Myles, Robert N. Feudale, Yang Liu, Nathaniel A. Woody, and Steven D. Brown. An introduction to decision tree modeling. Journal of Chemometrics, 18(6):275–285, 2004. doi:10.1002/cem.873.

94

Pang-Ning Tan, Michael Steinbach, and Vipin Kumar. Introduction to Data Mining. Addison Wesley, May 2005. ISBN 0321321367.

95

Leo Breiman. Random Forests. Machine Learning, 45(1):5–32, 2001. doi:10.1023/A:1010933404324.

96

scikit-learn documentation. Ensemble methods: random forests. URL: https://scikit-learn.org/stable/modules/ensemble.html#forests-of-randomized-trees.

97

TIBCO. What is a random forest? URL: https://www.tibco.com/reference-center/what-is-a-random-forest.

98

scikit-learn documentation. Sklearn.ensemble.gradientboostingclassifier. URL: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html.

99

Aratrika Pal. Gradient Boosting Trees for Classification: A Beginner’s Guide. 2020. URL: https://medium.com/swlh/gradient-boosting-trees-for-classification-a-beginners-guide-596b594a14ea.

100

scikit-learn documentation. Ensemble methods: gradient tree boosting. URL: https://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting.

101

1.2. linear and quadratic discriminant analysis. Accessed: 08-04-2022. URL: scikit-learn.org/stable/modules/lda_qda.html.

102

Quadratic discriminant analysis. 2021. Accessed: 08-04-2022. URL: https://towardsdatascience.com/quadratic-discriminant-analysis-ae55d8a8148a.

103

scikit-learn documentation. Clustering: k-means. 2021. Accessed: 09-04-2022. URL: https://scikit-learn.org/stable/modules/clustering.html#k-means.

104

Yuichiro Nakai. Searching for New Physics with Deep Autoencoders. 2019. Accessed: 28-04-2022. URL: https://indico.cern.ch/event/746178/contributions/3389042/attachments/1848494/3033674/AutoencoderV5.pdf.

105

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. http://www.deeplearningbook.org.

106

Marco Farina, Yuichiro Nakai, and David Shih. Searching for new physics with deep autoencoders. Physical Review D, 101:075021, Apr 2020. URL: https://link.aps.org/doi/10.1103/PhysRevD.101.075021, doi:10.1103/PhysRevD.101.075021.

107

Vinicius Mikuni and Florencia Canelli. Unsupervised clustering for collider physics. Physical Review D, may 2021. URL: https://doi.org/10.1103%2Fphysrevd.103.092007, doi:10.1103/physrevd.103.092007.

108

V. Mikuni and F. Canelli. ABCNet: an attention-based method for particle tagging. The European Physical Journal Plus, jun 2020. URL: https://doi.org/10.1140%2Fepjp%2Fs13360-020-00497-3, doi:10.1140/epjp/s13360-020-00497-3.

109

Georgios Choudalakis. On hypothesis testing, trials factor, hypertests and the bumphunter. 2011. URL: https://arxiv.org/abs/1101.0390, doi:10.48550/ARXIV.1101.0390.

110

Marina Sokolova and Guy Lapalme. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4):427–437, 2009. URL: https://www.sciencedirect.com/science/article/pii/S0306457309000259, doi:https://doi.org/10.1016/j.ipm.2009.03.002.

111

Andrea Valassi. Fisher information metrics for binary classifier evaluation and training. August 2018. URL: https://doi.org/10.5281/zenodo.1405727, doi:10.5281/zenodo.1405727.

112

Valassi, Andrea. Binary classifier metrics for optimizing hep event selection. EPJ Web of Conferences, 214:06004, 2019. URL: https://doi.org/10.1051/epjconf/201921406004, doi:10.1051/epjconf/201921406004.

113

Wikimedia Commons contributors. File:roc-draft-xkcd-style.svg. 2021. URL: https://commons.wikimedia.org/w/index.php?title=File:Roc-draft-xkcd-style.svg&oldid=588966985.

114

Matthias Kohl. Performance measures in binary classification. International Journal of Statistics in Medical Research, 1(1):79–81, Oct. 2012. URL: https://lifescienceglobal.com/pms/index.php/ijsmr/article/view/512, doi:10.6000/1929-6029.2012.01.01.08.

115

Tom Fawcett. ROC Graphs: Notes and Practical Considerations for Researchers. Machine Learning, 31:1–38, 01 2004.

116

scikit-learn documentation. Precision-Recall. URL: https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html.

117

G. Aad et al. Discrimination of Light Quark and Gluon Jets in $pp$ collisions at $\sqrt s = 8$ TeV with the ATLAS Detector. The European Physical Journal C, aug 2014. URL: https://doi.org/10.1140%2Fepjc%2Fs10052-014-3023-z, doi:10.1140/epjc/s10052-014-3023-z.

118

J. Donini, I. Dinu, and L. Vaslin. LHC Olympics Results Report. 2020. Accessed: 09-04-2022. URL: https://www.dropbox.com/s/mml3xk6c4ecd9qr/lhco_lpc%20-%20Ioan%20Dinu.pdf?dl=0.

119

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, 12:2825–2830, 2011.

120

Martín Abadi et al. TensorFlow: large-scale machine learning on heterogeneous systems. 2015. Software available from tensorflow.org. URL: https://www.tensorflow.org/.

121

ATLAS Collaboration. Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in ATLAS. Technical Report, CERN, Geneva, Jul 2018. URL: http://cds.cern.ch/record/2630973.

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8. Conclusión