Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC

ilustraciones, diagramas, fotografías

Autores:
Vinasco Soler, Rafael Andrei
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
eng
OAI Identifier:
oai:repositorio.unal.edu.co:unal/85588
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/85588
https://repositorio.unal.edu.co/
Palabra clave:
530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
Rights
openAccess
License
Reconocimiento 4.0 Internacional
id UNACIONAL2_abe94b522a7e689982b05e24875c9b0e
oai_identifier_str oai:repositorio.unal.edu.co:unal/85588
network_acronym_str UNACIONAL2
network_name_str Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
dc.title.translated.eng.fl_str_mv Use of the Lund plane technique for the identification of particles in ATLAS experiment at the LHC
title Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
spellingShingle Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
title_short Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_full Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_fullStr Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_full_unstemmed Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
title_sort Uso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHC
dc.creator.fl_str_mv Vinasco Soler, Rafael Andrei
dc.contributor.advisor.none.fl_str_mv Sandoval Usme, Carlos Eduardo
dc.contributor.author.none.fl_str_mv Vinasco Soler, Rafael Andrei
dc.contributor.researchgroup.spa.fl_str_mv Grupo de Partículas Fenyx-Un
dc.subject.ddc.spa.fl_str_mv 530 - Física
500 - Ciencias naturales y matemáticas
topic 530 - Física
500 - Ciencias naturales y matemáticas
W-tagging
Lund Jet plane
Machine learning
Identificación bosones W
Plano de Lund
Aprendizaje automático
Higgs boson
bosón de Higgs
W boson
bosón W
dc.subject.proposal.eng.fl_str_mv W-tagging
Lund Jet plane
Machine learning
dc.subject.proposal.spa.fl_str_mv Identificación bosones W
Plano de Lund
Aprendizaje automático
dc.subject.wikidata.none.fl_str_mv Higgs boson
bosón de Higgs
W boson
bosón W
description ilustraciones, diagramas, fotografías
publishDate 2023
dc.date.issued.none.fl_str_mv 2023-07-28
dc.date.accessioned.none.fl_str_mv 2024-02-01T16:13:37Z
dc.date.available.none.fl_str_mv 2024-02-01T16:13:37Z
dc.type.spa.fl_str_mv Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv Text
dc.type.redcol.spa.fl_str_mv http://purl.org/redcol/resource_type/TM
status_str acceptedVersion
dc.identifier.uri.none.fl_str_mv https://repositorio.unal.edu.co/handle/unal/85588
dc.identifier.instname.spa.fl_str_mv Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv https://repositorio.unal.edu.co/
url https://repositorio.unal.edu.co/handle/unal/85588
https://repositorio.unal.edu.co/
identifier_str_mv Universidad Nacional de Colombia
Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv C. S. Wu, E. Ambler, R. W. Hayward, D. D. Hoppes, and R. P. Hudson. Experimental test of parity conservation in beta decay. Phys. Rev., 105:1413–1415, Feb 1957.
C´assio Leite Vieira and Antonio Augusto Passos Videira. Carried by history: Cesar lattes, nuclear emulsions, and the discovery of the pi-meson. Physics in Perspective, 16(1):3–36, 2014.
F. Abe, H. Akimoto, A. Akopian, M. G. Albrow, S. R. Amendolia, D. Amidei, J. Antos, C. Anway-Wiese, S. Aota, G. Apollinari, and et al. Observation of top quark production inppcollisions with the collider detector at fermilab. Physical Review Letters, 74(14):2626–2631, Apr 1995.
Atlas Collaboration et al. Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc. arXiv preprint arXiv:1207.7214, 2012.
J Bendavid, F Caola, V Ciulli, R Harlander, G Heinrich, J Huston, S Kallweit, S Prestel, E Re, K Tackmann, et al. Les houches 2017: physics at tev colliders standard model working group report. arXiv preprint arXiv:1803.07977, 2018.
Eric Moreno, Olmo Cerri, Javier Duarte, Harvey Newman, Thong Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant. Jedi-net: a jet identification algorithm based on interaction networks. The European Physical Journal C, 80, 01 2020.
Agni Bethani, Alexander Bitadze, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Sam Dysch, Alessandra Forti, Emily Hanson, James Howarth, David Lack, et al. Performance of top-quark and ww-boson tagging with atlas in run 2 of the lhc. The Journal of High Energy Physics, 2019.
Bo Andersson, Gosta Gustafson, Leif Lonnblad, and Ulf Pettersson. Coherence efects in deep inelastic scattering. Zeitschrift fur Physik C Particles and Fields, 43(4):625–632, 1989.
Frédéric A Dreyer, Gavin P Salam, and Grégory Soyez. The lund jet plane. Journal of High Energy Physics, 2018(12):1–42, 2018.
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.
Matthias Fey and Jan Eric Lenssen. Fast Graph Representation Learning with PyTorch Geometric, 5 2019.
Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
ATLAS PUB Note. Identification of hadronically-decaying w bosons and top quarks using high-level features as input to boosted decision trees and deep neural networks in atlas at. 2017.
Atlas fact sheets. https://atlas.cern/Resources/Fact-sheets Accessed: 2022-09-07.
Georges Aad, E Abat, Jasmin Abdallah, AA Abdelalim, Abdelmalek Abdesselam, BA Abi, M Abolins, H Abramowicz, E Acerbi, BS Acharya, et al. The atlas experiment at the cern large hadron collider. Journal of instrumentation, 3(S08003), 2008.
Francesca Pastore, Atlas Collaboration, et al. Atlas run-2 status and performance. Nuclear and Particle Physics Proceedings, 270:3–7, 2016.
Yosuke Takubo. Atlas ibl operational experience. Technical report, ATL-COM-INDET- 2016-085, 2016.
Alessandro La Rosa, on behalf of ATLAS collaboration, et al. The atlas insertable b-layer: from construction to operation. Journal of Instrumentation, 11(12):C12036, 2016.
Georges Aad, Tatevik Abajyan, Brad Abbott, Jalal Abdallah, S Abdel Khalek, AA Abdelalim, R Aben, B Abi, M Abolins, OS AbouZeid, et al. Improved luminosity determination in pp collisions at ps = 7 TeV using the atlas detector at the lhc. The European Physical Journal C, 73(8):1–39, 2013.
C Goessling, R Klingenberg, D Muenstermann, A Rummler, G Troska, and T Wittig. Planar n+-in-n silicon pixel sensors for the atlas ibl upgrade. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 650(1):198–201, 2011.
Cinzia Da Via, Maurizio Boscardin, Gian-Franco Dalla Betta, Giovanni Darbo, Celeste Fleta, Claudia Gemme, Philippe Grenier, Sebastian Grinstein, Thor-Erik Hansen, Jasmine Hasi, et al. 3d silicon sensors: Design, large area production and quality assurance for the atlas ibl pixel detector upgrade. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 694:321–330, 2012.
Werner Herr and Bruno Muratori. Concept of luminosity. 2006.
ATLAS Collaboration. luminosity public results run 2. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/LuminosityPublicResultsRun2. Accessed: 2022-10-05.
Michael E Peskin. An introduction to quantum field theory. CRC press, 2018.
Georges Aad, B Abbott, J Abdallah, R Aben, M Abolins, OS AbouZeid, H Abramowicz, H Abreu, R Abreu, Y Abulaiti, et al. Constraints on new phenomena via higgs boson couplings and invisible decays with the atlas detector. Journal of High Energy Physics, 2015(11):1–52, 2015.
Stefano Giagu. Wimp dark matter searches with the atlas detector at the lhc. Frontiers in Physics, 7:75, 2019.
R Keith Ellis, W James Stirling, and Bryan R Webber. QCD and collider physics. Cambridge university press, 2003.
Peter Skands. Introduction to qcd. In Searching for New Physics at Small and Large Scales: TASI 2012, pages 341–420. World Scientific, 2013.
Masaharu Tanabashi, K Hagiwara, K Hikasa, Katsumasa Nakamura, Y Sumino, F Takahashi, J Tanaka, K Agashe, G Aielli, Claude Amsler, et al. Review of particle physics. Physical Review D, 98(3):030001, 2018.
W. N. Cottingham and D. A. Greenwood. An Introduction to the Standard Model of Particle Physics. Cambridge University Press, 2 edition, 2007.
Johan Alwall, Michel Herquet, Fabio Maltoni, Olivier Mattelaer, and Tim Stelzer. Madgraph 5: going beyond. Journal of High Energy Physics, 2011(6):1–40, 2011.
Alexander Pukhov. Calchep 2.3: Mssm, structure functions, event generation, batchs, and generation of matrix elements for other packages. arXiv preprint hep-ph/0412191, 2004.
Edward Boos, V Bunichev, M Dubinin, L Dudko, V Edneral, V Ilyin, A Kryukov, V Savrin, A Semenov, and A Sherstnev. Comphep 4.4—automatic computations from lagrangians to events. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 534(1-2):250–259, 2004.
Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip Ilten, Leif Lonnblad, Stephen Mrenna, Stefan Prestel, Christian T Preuss, et al. A comprehensive guide to the physics and usage of pythia 8.3. arXiv preprint arXiv:2203.11601, 2022.
Guido Altarelli and Giorgio Parisi. Asymptotic freedom in parton language. Nuclear Physics B, 126(2):298–318, 1977.
D Mason, J Brau, RB Drucker, R Frey, P Spentzouris, J Conrad, BT Fleming, J Formaggio, JH Kim, S Koutsoliotas, et al. Measurement of the nucleon strange-antistrange asymmetry at next-to-leading order in qcd from nutev dimuon data. Physical review letters, 99(19):192001, 2007.
Sergey Alekhin, Kirill Melnikov, and Frank Petriello. Fixed target drell-yan data and nnlo qcd fits of parton distribution functions. Physical Review D, 74(5):054033, 2006.
Sergey Alekhin, Simone Alioli, Richard D Ball, Valerio Bertone, Johannes Blumlein, Michiel Botje, Jon Butterworth, Francesco Cerutti, Amanda Cooper-Sarkar, Albert de Roeck, et al. The pdf4lhc working group interim report. arXiv preprint arXiv:1101.0536, 2011.
Torbjörn Sjöstrand. A model for initial state parton showers. Physics Letters B, 157(4):321–325, 1985.
Leif Lönnblad. Ariadne version 4—a program for simulation of qdc cascades implementing the colour dipole model. Computer Physics Communications, 71(1-2):15–31, 1992.
Gösta Gustafson and Ulf Pettersson. Dipole formulation of qcd cascades. Nuclear Physics B, 306(4):746–758, 1988.
Stefan Höche and Stefan Prestel. The midpoint between dipole and parton showers. The European Physical Journal C, 75(9):1–18, 2015.
Bo Andersson, Gösta Gustafson, and B Söderberg. A general model for jet fragmentation. Zeitschrift fur Physik C Particles and Fields, 20(4):317–329, 1983.
G Gorcella et al. Herwig 6.5: an event generator for hadron emmission reactions with interfering gluons (including supersymmetric processes). JHEP, 101:010, 2001.
Bryan R Webber. A qcd model for jet fragmentation including soft gluon interference. Nuclear Physics B, 238(3):492–528, 1984.
Morad Aaboud, G Aad, B Abbott, J Abdallah, Baptiste Abeloos, Syed Haide Abidi, OS AbouZeid, NL Abraham, Halina Abramowicz, Henso Abreu, et al. Jet reconstruction and performance using particle flow with the atlas detector. The European Physical Journal C, 77(7):1–47, 2017.
ATLAS collaboration et al. Improving jet substructure performance in atlas using track-caloclusters. Technical report, ATL-PHYS-PUB-2017-015, 2017.
Georges Aad, Brad Abbott, Dale Charles Abbott, A Abed Abud, Kira Abeling, Deshan Kavishka Abhayasinghe, Syed Haider Abidi, OS AbouZeid, NL Abraham, Halina Abramowicz, et al. Optimisation of large-radius jet reconstruction for the atlas detector in 13 tev proton–proton collisions. The European Physical Journal C, 81(4):1–47, 2021.
Gavin P Salam. Towards jetography. The European Physical Journal C, 67(3):637–686, 2010.
G Arnison, A Astbury, B Aubert, C Bacci, G Bauer, A Bezaguet, RK Bock, TJV Bowcock, M Calvetti, T Carroll, et al. Hadronic jet production at the cern protonantiproton collider. Physics Letters B, 132(1-3):214–222, 1983.
Gavin P Salam and Gregory Soyez. A practical seedless infrared-safe cone jet algorithm. Journal of High Energy Physics, 2007(05):086, 2007.
Stefano Catani, Yu L Dokshitzer, Michael H Seymour, and Bryan R Webber. Longitudinally-invariant k-clustering algorithms for hadron-hadron collisions. Nuclear Physics B, 406(1-2):187–224, 1993.
Matteo Cacciari, Gavin P Salam, and Gregory Soyez. The anti-kt jet clustering algorithm. Journal of High Energy Physics, 2008(04):063, 2008.
Markus Wobisch and Thorsten Wengler. Hadronization corrections to jet cross sections in deep-inelastic scattering. arXiv preprint hep-ph/9907280, 1999.
Ian Moult, Lina Necib, and Jesse Thaler. New angles on energy correlation functions. Journal of High Energy Physics, 2016(12):1–72, 2016.
Andrew J Larkoski, Simone Marzani, Gregory Soyez, and Jesse Thaler. Soft drop. Journal of High Energy Physics, 2014(5):1–46, 2014.
ATLAS Collaboration et al. Performance of w/z taggers using ufo jets in atlas, 2021.
Agni Bethani, Alexander Bitadze, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Sam Dysch, Alessandra Forti, Emily Hanson, James Howarth, David Lack, Ivan Lopez Paz, Jiri Masik, Stephen Menary, Francisca Munoz Sanchez, Alexander Oh, Joleen Pater, Yvonne Peters, Rebecca Pickles, Andrew Pilkington, Darren Price, Yang Qin, Jacob Rawling, Nicolas Scharmberg, Savanna Shaw, and Terence Wyatt. Performance of topquark and ww -boson tagging with atlas in run 2 of the lhc. The Journal of High Energy Physics, 2019.
Atlas Collaboration et al. Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in atlas. Technical report, ATL-PHYS-PUB-2018- 014, 2018.
Jesse Thaler and Ken Van Tilburg. Identifying boosted objects with n-subjettiness. Journal of High Energy Physics, 2011(3):1–28, 2011.
Geoffrey C Fox and Stephen Wolfram. Observables for the analysis of event shapes in e+ e- annihilation and other processes. Physical Review Letters, 41(23):1581, 1978.
Leandro G Almeida, Seung J Lee, Gilad Perez, Ilmo Sung, and Joseph Virzi. Top quark jets at the lhc. Physical Review D, 79(7):074012, 2009.
Georges Aad, B Abbott, J Abdallah, S Abdel Khalek, AA Abdelalim, B Abi, M Abolins, OS AbouZeid, H Abramowicz, H Abreu, et al. Atlas measurements of the properties of jets for boosted particle searches. Physical Review D, 86(7):072006, 2012.
Chunhui Chen. New approach to identifying boosted hadronically decaying particles using jet substructure in its center-of-mass frame. Physical Review D, 85(3):034007, 2012.
Georges Aad, Tatevik Abajyan, Brad Abbott, Jalal Abdallah, S Abdel Khalek, AA Abdelalim, R Aben, B Abi, M Abolins, OS AbouZeid, et al. Measurement of k t splitting scales in w ! lw events at ps = 7TeV with the atlas detector. The European Physical Journal C, 73(5):1–30, 2013.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, DavidWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.
Christopher M Bishop. Mixture density networks. 1994.
ATLAS Collaboration et al. Identification of boosted higgs bosons decaying into bb with neural networks and variable radius subjets in atlas, 2020.
Francesco Armando Di Bello. Optimisation of the atlas b-tagging algorithms for the 2017-2018 lhc data-taking. Technical report, ATL-COM-PHYS-2017-1472, 2017.
Frédéric A Dreyer and Huilin Qu. Jet tagging in the lund plane with graph networks. Journal of High Energy Physics, 2021(3):1–23, 2021.
Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
Yurii Nesterov. A method for unconstrained convex minimization problem with the rate of convergence o (1/k 2). In Doklady an ussr, volume 269, pages 543–547, 1983.
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18–42, 2017.
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1–12, 2019.
Gilles Louppe, Michael Kagan, and Kyle Cranmer. Learning to pivot with adversarial networks. Advances in neural information processing systems, 30, 2017.
Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pages 1180–1189. PMLR, 2015.
Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79–86, 1951.
Atlas Collaboration et al. Measurement of the lund jet plane using charged particles in 13 tev proton-proton collisions with the atlas detector. arXiv preprint arXiv:2004.03540, 2020.
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks. stat, 1050(20):10–48550, 2017.
Jesse Thaler and Lian-Tao Wang. Strategies to identify boosted tops. Journal of High Energy Physics, 2008(07):092, jul 2008.
Enrico Bothmann, Gurpreet Singh Chahal, Stefan Hoche, Johannes Krause, Frank Krauss, Silvan Kuttimalai, Sebastian Liebschner, Davide Napoletano, Marek Schonherr, Holger Schulz, Stefen Schumann, and Frank Siegert. Event generation with Sherpa 2.2. SciPost Phys., 7:034, 2019.
J-C Winter, Frank Krauss, and Gerhard Sof. A modified cluster-hadronisation model. The European Physical Journal C-Particles and Fields, 36:381–395, 2004.
B. Andersson, G. Gustafson, G. Ingelman, and T. Sjostrand. Parton fragmentation and string dynamics. Physics Reports, 97(2):31–145, 1983.
Torbjorn Sjostrand. Jet fragmentation of multiparton configurations in a string framework. Nuclear Physics B, 248(2):469–502, 1984.
Johannes Bellm, Stefan Gieseke, David Grellscheid, Simon Platzer, Michael Rauch, Christian Reuschle, Peter Richardson, Peter Schichtel, Michael H Seymour, Andrzej Si´odmok, et al. Herwig 7.0/herwig++ 3.0 release note. The European Physical Journal C, 76:1–8, 2016.
Manuel Bahr, Stefan Gieseke, Martyn A Gigg, David Grellscheid, Keith Hamilton, Oluseyi Latunde-Dada, Simon Platzer, Peter Richardson, Michael H Seymour, Alexander Sherstnev, et al. Herwig++ physics and manual. The European Physical Journal C, 58:639–707, 2008.
Lucian A Harland-Lang, AD Martin, P Motylinski, and RS Thorne. Parton distributions in the lhc era: Mmht 2014 pdfs. The European Physical Journal C, 75(5):204, 2015.
Tagging boosted W bosons applying machine learning to the Lund Jet Plane. Technical report, CERN, Geneva, 2023. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATLPHYS- PUB-2023-017.
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Reconocimiento 4.0 Internacional
dc.rights.uri.spa.fl_str_mv http://creativecommons.org/licenses/by/4.0/
dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv Reconocimiento 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.extent.spa.fl_str_mv xi, 110 paginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.spa.fl_str_mv Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv Bogotá - Ciencias - Maestría en Ciencias - Física
dc.publisher.faculty.spa.fl_str_mv Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv Universidad Nacional de Colombia - Sede Bogotá
institution Universidad Nacional de Colombia
bitstream.url.fl_str_mv https://repositorio.unal.edu.co/bitstream/unal/85588/1/license.txt
https://repositorio.unal.edu.co/bitstream/unal/85588/2/1030646597.2023.pdf
bitstream.checksum.fl_str_mv eb34b1cf90b7e1103fc9dfd26be24b4a
d629c031f303cd8a21a588707490f292
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
repository.name.fl_str_mv Repositorio Institucional Universidad Nacional de Colombia
repository.mail.fl_str_mv repositorio_nal@unal.edu.co
_version_ 1806885963664719872
spelling Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sandoval Usme, Carlos Eduardo1fb44a8317231208c66f0f92600dbfd2Vinasco Soler, Rafael Andrei765e2d0f9ec9dfd2b448a01d24a3b3bfGrupo de Partículas Fenyx-Un2024-02-01T16:13:37Z2024-02-01T16:13:37Z2023-07-28https://repositorio.unal.edu.co/handle/unal/85588Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramas, fotografíasSe estudia el uso de las variables del Plano de Lund como entradas para algoritmos de aprendizaje automático basados en Graph neural Networks (GNN) para la identificación de jets provenientes de bosones W y bosones de Higgs con alto momento transverso en el experimento del ATLAS. Se realizó el análisis por separado para bosones W y para bosones de Higgs. Para bosones W se probaron 5 diferentes arquitecturas de GNN, además para la arquitectura con mejor comportamiento se estudia la inclusión de una red adversaria con el propósito de obtener un identificador de partículas no dependiente de la masa. Para el bosón de Higgs se realizaron los estudios con el propósito de detectar únicamente el canal de decaimiento H a bb, para este caso 2 arquitecturas fueron estudiadas, una siendo un modelo basado únicamente en GNN que usa variables del plano de Lund y otra un modelo mixto que usa las variables del plano de Lund y además el puntaje del identificador actualmente usado en el experimento del ATLAS. Se obtuvo como resultado para los identificadores de bosones W que 4 de ellos tienen un rechazo de jets no deseados mayor a los identificadores encontrados en la literatura actualmente usados, además para el clasificador entrenado junto a una red adversaria se obtuvo una excelente decorrelación además de mantener un rechazo de eventos no deseados de más del doble en comparación al tagger decorrelacionado de la masa reportado en la literatura. Para el caso de identificación de bosones de Higgs se encontró un pobre rendimiento del algoritmo que únicamente usa las variables del Plano de Lund, pero para el algoritmo mixto se obtiene una mejora de alrededor de un orden de magnitud en el rechazo de los jets no deseados. Para el clasificador mixto entrenado junto a una red adversaria se obtuvo una decorrelación buena con un rechazo de jets de no deseados 3 veces mayor al tagger actualmente usado reportado.The use of the Lund Plane variables as inputs for machine learning algorithms based on Graph neural Networks (GNN) for the identification of jets coming from W bosons and Higgs bosons with high transverse moment in the ATLAS experiment is studied. The analisis was performed separately for W bosons and for Higgs bosons. For W bosons, 5 different GNN architectures were tested, for the architecture with the best rejection of unwanted jets the inclusion of an adversarial network during the training is studied, in order to obtain a tagger that does not depend on mass. For the Higgs boson, the studies are done with the purpose of tag only the decay channel H to bb, for this case 2 architectures were studied, one being a model based on GNN that uses variables of the Lund plane and the other proposed is a mixed model that uses the Lund plane variables and also the score of the tagger currently used in the ATLAS experiment. It was obtained for the W boson taggers that 4 of the taggers proposed have a greater rejection of unwanted jets than the currently taggers used found in the literature. In addition, the tagger trained together with an adversarial network obtain an excellent decorrelation with a rejection of unwanted jets of more tan double compared to the mass decorrelated taggers reported in the literature. For the case of Higgs boson identification, a poor performance of the algorithm that only uses the variables of the Lund Plane was found compared to current tagger, but for the mixed algorithm an improvement of around an order of magnitude is obtained in the rejection of unwanted jets. For the classifier trained together with an adversarial network a good decorrelation is obtained, with a rejection of unwanted jets 3 times higher than the currently used tagger reported.MaestríaMagíster en Ciencias - Físicaxi, 110 paginasapplication/pdfengUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - FísicaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá530 - Física500 - Ciencias naturales y matemáticasW-taggingLund Jet planeMachine learningIdentificación bosones WPlano de LundAprendizaje automáticoHiggs bosonbosón de HiggsW bosonbosón WUso de la técnica del plano de Lund para la identificación de partículas en el experímento ATLAS del LHCUse of the Lund plane technique for the identification of particles in ATLAS experiment at the LHCTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMC. S. Wu, E. Ambler, R. W. Hayward, D. D. Hoppes, and R. P. Hudson. Experimental test of parity conservation in beta decay. Phys. Rev., 105:1413–1415, Feb 1957.C´assio Leite Vieira and Antonio Augusto Passos Videira. Carried by history: Cesar lattes, nuclear emulsions, and the discovery of the pi-meson. Physics in Perspective, 16(1):3–36, 2014.F. Abe, H. Akimoto, A. Akopian, M. G. Albrow, S. R. Amendolia, D. Amidei, J. Antos, C. Anway-Wiese, S. Aota, G. Apollinari, and et al. Observation of top quark production inppcollisions with the collider detector at fermilab. Physical Review Letters, 74(14):2626–2631, Apr 1995.Atlas Collaboration et al. Observation of a new particle in the search for the standard model higgs boson with the atlas detector at the lhc. arXiv preprint arXiv:1207.7214, 2012.J Bendavid, F Caola, V Ciulli, R Harlander, G Heinrich, J Huston, S Kallweit, S Prestel, E Re, K Tackmann, et al. Les houches 2017: physics at tev colliders standard model working group report. arXiv preprint arXiv:1803.07977, 2018.Eric Moreno, Olmo Cerri, Javier Duarte, Harvey Newman, Thong Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant. Jedi-net: a jet identification algorithm based on interaction networks. The European Physical Journal C, 80, 01 2020.Agni Bethani, Alexander Bitadze, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Sam Dysch, Alessandra Forti, Emily Hanson, James Howarth, David Lack, et al. Performance of top-quark and ww-boson tagging with atlas in run 2 of the lhc. The Journal of High Energy Physics, 2019.Bo Andersson, Gosta Gustafson, Leif Lonnblad, and Ulf Pettersson. Coherence efects in deep inelastic scattering. Zeitschrift fur Physik C Particles and Fields, 43(4):625–632, 1989.Frédéric A Dreyer, Gavin P Salam, and Grégory Soyez. The lund jet plane. Journal of High Energy Physics, 2018(12):1–42, 2018.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.Matthias Fey and Jan Eric Lenssen. Fast Graph Representation Learning with PyTorch Geometric, 5 2019.Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.ATLAS PUB Note. Identification of hadronically-decaying w bosons and top quarks using high-level features as input to boosted decision trees and deep neural networks in atlas at. 2017.Atlas fact sheets. https://atlas.cern/Resources/Fact-sheets Accessed: 2022-09-07.Georges Aad, E Abat, Jasmin Abdallah, AA Abdelalim, Abdelmalek Abdesselam, BA Abi, M Abolins, H Abramowicz, E Acerbi, BS Acharya, et al. The atlas experiment at the cern large hadron collider. Journal of instrumentation, 3(S08003), 2008.Francesca Pastore, Atlas Collaboration, et al. Atlas run-2 status and performance. Nuclear and Particle Physics Proceedings, 270:3–7, 2016.Yosuke Takubo. Atlas ibl operational experience. Technical report, ATL-COM-INDET- 2016-085, 2016.Alessandro La Rosa, on behalf of ATLAS collaboration, et al. The atlas insertable b-layer: from construction to operation. Journal of Instrumentation, 11(12):C12036, 2016.Georges Aad, Tatevik Abajyan, Brad Abbott, Jalal Abdallah, S Abdel Khalek, AA Abdelalim, R Aben, B Abi, M Abolins, OS AbouZeid, et al. Improved luminosity determination in pp collisions at ps = 7 TeV using the atlas detector at the lhc. The European Physical Journal C, 73(8):1–39, 2013.C Goessling, R Klingenberg, D Muenstermann, A Rummler, G Troska, and T Wittig. Planar n+-in-n silicon pixel sensors for the atlas ibl upgrade. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 650(1):198–201, 2011.Cinzia Da Via, Maurizio Boscardin, Gian-Franco Dalla Betta, Giovanni Darbo, Celeste Fleta, Claudia Gemme, Philippe Grenier, Sebastian Grinstein, Thor-Erik Hansen, Jasmine Hasi, et al. 3d silicon sensors: Design, large area production and quality assurance for the atlas ibl pixel detector upgrade. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 694:321–330, 2012.Werner Herr and Bruno Muratori. Concept of luminosity. 2006.ATLAS Collaboration. luminosity public results run 2. https://twiki.cern.ch/twiki/bin/view/AtlasPublic/LuminosityPublicResultsRun2. Accessed: 2022-10-05.Michael E Peskin. An introduction to quantum field theory. CRC press, 2018.Georges Aad, B Abbott, J Abdallah, R Aben, M Abolins, OS AbouZeid, H Abramowicz, H Abreu, R Abreu, Y Abulaiti, et al. Constraints on new phenomena via higgs boson couplings and invisible decays with the atlas detector. Journal of High Energy Physics, 2015(11):1–52, 2015.Stefano Giagu. Wimp dark matter searches with the atlas detector at the lhc. Frontiers in Physics, 7:75, 2019.R Keith Ellis, W James Stirling, and Bryan R Webber. QCD and collider physics. Cambridge university press, 2003.Peter Skands. Introduction to qcd. In Searching for New Physics at Small and Large Scales: TASI 2012, pages 341–420. World Scientific, 2013.Masaharu Tanabashi, K Hagiwara, K Hikasa, Katsumasa Nakamura, Y Sumino, F Takahashi, J Tanaka, K Agashe, G Aielli, Claude Amsler, et al. Review of particle physics. Physical Review D, 98(3):030001, 2018.W. N. Cottingham and D. A. Greenwood. An Introduction to the Standard Model of Particle Physics. Cambridge University Press, 2 edition, 2007.Johan Alwall, Michel Herquet, Fabio Maltoni, Olivier Mattelaer, and Tim Stelzer. Madgraph 5: going beyond. Journal of High Energy Physics, 2011(6):1–40, 2011.Alexander Pukhov. Calchep 2.3: Mssm, structure functions, event generation, batchs, and generation of matrix elements for other packages. arXiv preprint hep-ph/0412191, 2004.Edward Boos, V Bunichev, M Dubinin, L Dudko, V Edneral, V Ilyin, A Kryukov, V Savrin, A Semenov, and A Sherstnev. Comphep 4.4—automatic computations from lagrangians to events. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 534(1-2):250–259, 2004.Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip Ilten, Leif Lonnblad, Stephen Mrenna, Stefan Prestel, Christian T Preuss, et al. A comprehensive guide to the physics and usage of pythia 8.3. arXiv preprint arXiv:2203.11601, 2022.Guido Altarelli and Giorgio Parisi. Asymptotic freedom in parton language. Nuclear Physics B, 126(2):298–318, 1977.D Mason, J Brau, RB Drucker, R Frey, P Spentzouris, J Conrad, BT Fleming, J Formaggio, JH Kim, S Koutsoliotas, et al. Measurement of the nucleon strange-antistrange asymmetry at next-to-leading order in qcd from nutev dimuon data. Physical review letters, 99(19):192001, 2007.Sergey Alekhin, Kirill Melnikov, and Frank Petriello. Fixed target drell-yan data and nnlo qcd fits of parton distribution functions. Physical Review D, 74(5):054033, 2006.Sergey Alekhin, Simone Alioli, Richard D Ball, Valerio Bertone, Johannes Blumlein, Michiel Botje, Jon Butterworth, Francesco Cerutti, Amanda Cooper-Sarkar, Albert de Roeck, et al. The pdf4lhc working group interim report. arXiv preprint arXiv:1101.0536, 2011.Torbjörn Sjöstrand. A model for initial state parton showers. Physics Letters B, 157(4):321–325, 1985.Leif Lönnblad. Ariadne version 4—a program for simulation of qdc cascades implementing the colour dipole model. Computer Physics Communications, 71(1-2):15–31, 1992.Gösta Gustafson and Ulf Pettersson. Dipole formulation of qcd cascades. Nuclear Physics B, 306(4):746–758, 1988.Stefan Höche and Stefan Prestel. The midpoint between dipole and parton showers. The European Physical Journal C, 75(9):1–18, 2015.Bo Andersson, Gösta Gustafson, and B Söderberg. A general model for jet fragmentation. Zeitschrift fur Physik C Particles and Fields, 20(4):317–329, 1983.G Gorcella et al. Herwig 6.5: an event generator for hadron emmission reactions with interfering gluons (including supersymmetric processes). JHEP, 101:010, 2001.Bryan R Webber. A qcd model for jet fragmentation including soft gluon interference. Nuclear Physics B, 238(3):492–528, 1984.Morad Aaboud, G Aad, B Abbott, J Abdallah, Baptiste Abeloos, Syed Haide Abidi, OS AbouZeid, NL Abraham, Halina Abramowicz, Henso Abreu, et al. Jet reconstruction and performance using particle flow with the atlas detector. The European Physical Journal C, 77(7):1–47, 2017.ATLAS collaboration et al. Improving jet substructure performance in atlas using track-caloclusters. Technical report, ATL-PHYS-PUB-2017-015, 2017.Georges Aad, Brad Abbott, Dale Charles Abbott, A Abed Abud, Kira Abeling, Deshan Kavishka Abhayasinghe, Syed Haider Abidi, OS AbouZeid, NL Abraham, Halina Abramowicz, et al. Optimisation of large-radius jet reconstruction for the atlas detector in 13 tev proton–proton collisions. The European Physical Journal C, 81(4):1–47, 2021.Gavin P Salam. Towards jetography. The European Physical Journal C, 67(3):637–686, 2010.G Arnison, A Astbury, B Aubert, C Bacci, G Bauer, A Bezaguet, RK Bock, TJV Bowcock, M Calvetti, T Carroll, et al. Hadronic jet production at the cern protonantiproton collider. Physics Letters B, 132(1-3):214–222, 1983.Gavin P Salam and Gregory Soyez. A practical seedless infrared-safe cone jet algorithm. Journal of High Energy Physics, 2007(05):086, 2007.Stefano Catani, Yu L Dokshitzer, Michael H Seymour, and Bryan R Webber. Longitudinally-invariant k-clustering algorithms for hadron-hadron collisions. Nuclear Physics B, 406(1-2):187–224, 1993.Matteo Cacciari, Gavin P Salam, and Gregory Soyez. The anti-kt jet clustering algorithm. Journal of High Energy Physics, 2008(04):063, 2008.Markus Wobisch and Thorsten Wengler. Hadronization corrections to jet cross sections in deep-inelastic scattering. arXiv preprint hep-ph/9907280, 1999.Ian Moult, Lina Necib, and Jesse Thaler. New angles on energy correlation functions. Journal of High Energy Physics, 2016(12):1–72, 2016.Andrew J Larkoski, Simone Marzani, Gregory Soyez, and Jesse Thaler. Soft drop. Journal of High Energy Physics, 2014(5):1–46, 2014.ATLAS Collaboration et al. Performance of w/z taggers using ufo jets in atlas, 2021.Agni Bethani, Alexander Bitadze, Jonathan Crane, Cinzia Da Via, Nicholas Dann, Sam Dysch, Alessandra Forti, Emily Hanson, James Howarth, David Lack, Ivan Lopez Paz, Jiri Masik, Stephen Menary, Francisca Munoz Sanchez, Alexander Oh, Joleen Pater, Yvonne Peters, Rebecca Pickles, Andrew Pilkington, Darren Price, Yang Qin, Jacob Rawling, Nicolas Scharmberg, Savanna Shaw, and Terence Wyatt. Performance of topquark and ww -boson tagging with atlas in run 2 of the lhc. The Journal of High Energy Physics, 2019.Atlas Collaboration et al. Performance of mass-decorrelated jet substructure observables for hadronic two-body decay tagging in atlas. Technical report, ATL-PHYS-PUB-2018- 014, 2018.Jesse Thaler and Ken Van Tilburg. Identifying boosted objects with n-subjettiness. Journal of High Energy Physics, 2011(3):1–28, 2011.Geoffrey C Fox and Stephen Wolfram. Observables for the analysis of event shapes in e+ e- annihilation and other processes. Physical Review Letters, 41(23):1581, 1978.Leandro G Almeida, Seung J Lee, Gilad Perez, Ilmo Sung, and Joseph Virzi. Top quark jets at the lhc. Physical Review D, 79(7):074012, 2009.Georges Aad, B Abbott, J Abdallah, S Abdel Khalek, AA Abdelalim, B Abi, M Abolins, OS AbouZeid, H Abramowicz, H Abreu, et al. Atlas measurements of the properties of jets for boosted particle searches. Physical Review D, 86(7):072006, 2012.Chunhui Chen. New approach to identifying boosted hadronically decaying particles using jet substructure in its center-of-mass frame. Physical Review D, 85(3):034007, 2012.Georges Aad, Tatevik Abajyan, Brad Abbott, Jalal Abdallah, S Abdel Khalek, AA Abdelalim, R Aben, B Abi, M Abolins, OS AbouZeid, et al. Measurement of k t splitting scales in w ! lw events at ps = 7TeV with the atlas detector. The European Physical Journal C, 73(5):1–30, 2013.Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, DavidWarde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014.Christopher M Bishop. Mixture density networks. 1994.ATLAS Collaboration et al. Identification of boosted higgs bosons decaying into bb with neural networks and variable radius subjets in atlas, 2020.Francesco Armando Di Bello. Optimisation of the atlas b-tagging algorithms for the 2017-2018 lhc data-taking. Technical report, ATL-COM-PHYS-2017-1472, 2017.Frédéric A Dreyer and Huilin Qu. Jet tagging in the lund plane with graph networks. Journal of High Energy Physics, 2021(3):1–23, 2021.Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.Yurii Nesterov. A method for unconstrained convex minimization problem with the rate of convergence o (1/k 2). In Doklady an ussr, volume 269, pages 543–547, 1983.Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826, 2018.Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. Geometric deep learning: going beyond euclidean data. IEEE Signal Processing Magazine, 34(4):18–42, 2017.Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1–12, 2019.Gilles Louppe, Michael Kagan, and Kyle Cranmer. Learning to pivot with adversarial networks. Advances in neural information processing systems, 30, 2017.Yaroslav Ganin and Victor Lempitsky. Unsupervised domain adaptation by backpropagation. In International conference on machine learning, pages 1180–1189. PMLR, 2015.Solomon Kullback and Richard A Leibler. On information and sufficiency. The annals of mathematical statistics, 22(1):79–86, 1951.Atlas Collaboration et al. Measurement of the lund jet plane using charged particles in 13 tev proton-proton collisions with the atlas detector. arXiv preprint arXiv:2004.03540, 2020.Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio, et al. Graph attention networks. stat, 1050(20):10–48550, 2017.Jesse Thaler and Lian-Tao Wang. Strategies to identify boosted tops. Journal of High Energy Physics, 2008(07):092, jul 2008.Enrico Bothmann, Gurpreet Singh Chahal, Stefan Hoche, Johannes Krause, Frank Krauss, Silvan Kuttimalai, Sebastian Liebschner, Davide Napoletano, Marek Schonherr, Holger Schulz, Stefen Schumann, and Frank Siegert. Event generation with Sherpa 2.2. SciPost Phys., 7:034, 2019.J-C Winter, Frank Krauss, and Gerhard Sof. A modified cluster-hadronisation model. The European Physical Journal C-Particles and Fields, 36:381–395, 2004.B. Andersson, G. Gustafson, G. Ingelman, and T. Sjostrand. Parton fragmentation and string dynamics. Physics Reports, 97(2):31–145, 1983.Torbjorn Sjostrand. Jet fragmentation of multiparton configurations in a string framework. Nuclear Physics B, 248(2):469–502, 1984.Johannes Bellm, Stefan Gieseke, David Grellscheid, Simon Platzer, Michael Rauch, Christian Reuschle, Peter Richardson, Peter Schichtel, Michael H Seymour, Andrzej Si´odmok, et al. Herwig 7.0/herwig++ 3.0 release note. The European Physical Journal C, 76:1–8, 2016.Manuel Bahr, Stefan Gieseke, Martyn A Gigg, David Grellscheid, Keith Hamilton, Oluseyi Latunde-Dada, Simon Platzer, Peter Richardson, Michael H Seymour, Alexander Sherstnev, et al. Herwig++ physics and manual. The European Physical Journal C, 58:639–707, 2008.Lucian A Harland-Lang, AD Martin, P Motylinski, and RS Thorne. Parton distributions in the lhc era: Mmht 2014 pdfs. The European Physical Journal C, 75(5):204, 2015.Tagging boosted W bosons applying machine learning to the Lund Jet Plane. Technical report, CERN, Geneva, 2023. All figures including auxiliary figures are available at https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PUBNOTES/ATLPHYS- PUB-2023-017.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85588/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1030646597.2023.pdf1030646597.2023.pdfTesis de Maestría en Físicaapplication/pdf16076315https://repositorio.unal.edu.co/bitstream/unal/85588/2/1030646597.2023.pdfd629c031f303cd8a21a588707490f292MD52unal/85588oai:repositorio.unal.edu.co:unal/855882024-02-01 11:42:10.476Repositorio Institucional Universidad Nacional de Colombiarepositorio_nal@unal.edu.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

Publicaciones similares