GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo
Esta tesis desarrolla un árbol de decisión de redes neuronales convolucionales (CNNs), llamada GSpyNetTree, usada por la colaboración LIGO-Virgo para distinguir señales de ondas gravitacionales de ruido de detector.
- Autores:
-
Álvarez López, María Sofía
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/68749
- Acceso en línea:
- http://hdl.handle.net/1992/68749
- Palabra clave:
- Machine Learning
Ondas gravitacionales
LIGO
Convolutional Neural Networks
CNNs
Ingeniería
- Rights
- openAccess
- License
- Attribution-NoDerivatives 4.0 Internacional
| id |
UNIANDES2_2895ca7072e881026a734b24d2827aca |
|---|---|
| oai_identifier_str |
oai:repositorio.uniandes.edu.co:1992/68749 |
| network_acronym_str |
UNIANDES2 |
| network_name_str |
Séneca: repositorio Uniandes |
| repository_id_str |
|
| dc.title.none.fl_str_mv |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| title |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| spellingShingle |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo Machine Learning Ondas gravitacionales LIGO Convolutional Neural Networks CNNs Ingeniería |
| title_short |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| title_full |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| title_fullStr |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| title_full_unstemmed |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| title_sort |
GSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-Virgo |
| dc.creator.fl_str_mv |
Álvarez López, María Sofía |
| dc.contributor.advisor.none.fl_str_mv |
Núñez Castro, Haydemar María |
| dc.contributor.author.none.fl_str_mv |
Álvarez López, María Sofía |
| dc.contributor.researchgroup.es_CO.fl_str_mv |
COMIT |
| dc.subject.keyword.none.fl_str_mv |
Machine Learning Ondas gravitacionales LIGO Convolutional Neural Networks CNNs |
| topic |
Machine Learning Ondas gravitacionales LIGO Convolutional Neural Networks CNNs Ingeniería |
| dc.subject.themes.es_CO.fl_str_mv |
Ingeniería |
| description |
Esta tesis desarrolla un árbol de decisión de redes neuronales convolucionales (CNNs), llamada GSpyNetTree, usada por la colaboración LIGO-Virgo para distinguir señales de ondas gravitacionales de ruido de detector. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-07-25T20:45:17Z |
| dc.date.available.none.fl_str_mv |
2023-07-25T20:45:17Z |
| dc.date.issued.none.fl_str_mv |
2023-07-16 |
| dc.type.es_CO.fl_str_mv |
Trabajo de grado - Pregrado |
| dc.type.driver.none.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
| dc.type.version.none.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
| dc.type.coar.none.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
| dc.type.content.es_CO.fl_str_mv |
Text |
| dc.type.redcol.none.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
| format |
http://purl.org/coar/resource_type/c_7a1f |
| status_str |
acceptedVersion |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/1992/68749 |
| dc.identifier.instname.es_CO.fl_str_mv |
instname:Universidad de los Andes |
| dc.identifier.reponame.es_CO.fl_str_mv |
reponame:Repositorio Institucional Séneca |
| dc.identifier.repourl.es_CO.fl_str_mv |
repourl:https://repositorio.uniandes.edu.co/ |
| url |
http://hdl.handle.net/1992/68749 |
| identifier_str_mv |
instname:Universidad de los Andes reponame:Repositorio Institucional Séneca repourl:https://repositorio.uniandes.edu.co/ |
| dc.language.iso.es_CO.fl_str_mv |
spa |
| language |
spa |
| dc.relation.references.es_CO.fl_str_mv |
B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «Observation of Gravitational Waves from a Binary Black Hole Merger», Phys. Rev. Lett. 116, 061102 (2016). J. McIver y D. H. Shoemaker, «Discovering gravitational waves with Advanced LIGO», Contemporary Physics 61, 229-255 (2020). M. Zevin et al., «Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science», Classical and Quantum Gravity 34, arXiv:1611.04596 [astro-ph, physics:gr-qc, physics:physics], 064003 (2017). B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GW150914: The Advanced LIGO Detectors in the Era of First Discoveries», Phys. Rev. Lett. 116, 131103 (2016). B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs», en, 10.1103/PhysRevX.9.031040. B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run», en, 10.1103/PhysRevX.11.021053. B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration), «GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run», en, 10.48550/arXiv.2111.03606. LIGO Scientific Collaboration and Virgo Collaboration, LIGO Laboratory at Caltech and MIT. P. R. Saulson, Fundamentals of interferometric gravitational wave detectors (World Scientific, 1994). The LIGO Scientific Collaboration, What is an interferometer? J. Harms, «Terrestrial Gravity Fluctuations», Living Reviews in Relativity 22, arXiv:1507.05850 [gr-qc], 6 (2019). The LIGO Scientific Collaboration and the Virgo Collaboration, «Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914», Classical and Quantum Gravity 33, arXiv:1602.03844 [astro-ph, physics:gr-qc, physics:physics], 134001 (2016). S. Anderson, K. Burtnyk y J. Kanner, LIGO-M1000066-v27: LIGO Data Management Plan, 2022. B. Berger, «Identification and mitigation of Advanced LIGO noise sources», Journal of Physics: Conference Series 957, 012004 (2018) D. Davis et al., «LIGO Detector Characterization in the Second and Third Observing Runs», Classical and Quantum Gravity 38, arXiv:2101.11673 [astro-ph, physics:gr-qc], 135014 (2021). J. S. Areeda et al., LigoDV-web: Providing easy, secure and universal access to a large distributed scientific data store for the LIGO Scientific Collaboration, arXiv:1611.01089 [astro-ph, physics:gr-qc], nov. de 2016. Jarov, S. et al., «A new method to distinguish gravitational-wave signals from detector glitches with Gravity Spy». S. Alvarez-Lopez et al., GSpyNetTree: A signal-vs-glitch classifier for gravitational-wave event candidates, 2023. A. Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : 2nd ed., https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ (O'Reilly Media, Inc., Mumbai, 2020). A. C. Wilson et al., The Marginal Value of Adaptive Gradient Methods in Machine Learning, 2018. D. Masters y C. Luschi, Revisiting Small Batch Training for Deep Neural Networks, 2018. IBM, What are convolutional neural networks? LIGO Scientific Collaboration, LIGO Algorithm Library - LALSuite, free software (GPL), 2018. S. Husa et al., «Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal», Phys. Rev. D 93, 044006 (2016). S. Khan et al., «Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era», Phys. Rev. D 93, 044007 (2016). Pace A, Prestegard T, Moe B and Stephens B, «GraceDB Gravitational-Wave Candidate Event Database», https://gracedb.ligo.org/ (2020). The LIGO Scientific Collaboration and The Virgo Collaboration, «LIGO/Virgo Alert System (LVAlert)» M. Tan y Q. V. Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, 2020. C. Szegedy et al., «Rethinking the Inception Architecture for Computer Vision», en 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), págs. 2818-2826. C. Szegedy et al., «Going deeper with convolutions», en 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), págs. 1-9. |
| dc.rights.license.spa.fl_str_mv |
Attribution-NoDerivatives 4.0 Internacional |
| dc.rights.uri.*.fl_str_mv |
http://creativecommons.org/licenses/by-nd/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
| rights_invalid_str_mv |
Attribution-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nd/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.extent.es_CO.fl_str_mv |
49 páginas |
| dc.format.mimetype.es_CO.fl_str_mv |
application/pdf |
| dc.publisher.es_CO.fl_str_mv |
Universidad de los Andes |
| dc.publisher.program.es_CO.fl_str_mv |
Ingeniería de Sistemas y Computación |
| dc.publisher.faculty.es_CO.fl_str_mv |
Facultad de Ingeniería |
| dc.publisher.department.es_CO.fl_str_mv |
Departamento de Ingeniería Sistemas y Computación |
| institution |
Universidad de los Andes |
| bitstream.url.fl_str_mv |
https://repositorio.uniandes.edu.co/bitstreams/b1e43b3b-200e-47da-8556-dfc231a0d387/download https://repositorio.uniandes.edu.co/bitstreams/a2d8ceb4-20d7-4ad2-9217-e3d07d4a5f94/download https://repositorio.uniandes.edu.co/bitstreams/be2618da-8b06-4c79-95d4-c86b8ee8ab2d/download https://repositorio.uniandes.edu.co/bitstreams/d24034f0-d0ee-4b8c-b428-5360d5652ac9/download https://repositorio.uniandes.edu.co/bitstreams/fa8310c4-b4e7-4c0c-af4b-cf5d7e95389c/download https://repositorio.uniandes.edu.co/bitstreams/d8ba35c8-6d0f-42ed-adaa-d8a40a1cfb3c/download https://repositorio.uniandes.edu.co/bitstreams/8e445e72-637b-4252-abb4-dc24995129bc/download https://repositorio.uniandes.edu.co/bitstreams/dfa904c6-6a9c-49dd-8167-a11ca9e75b6a/download |
| bitstream.checksum.fl_str_mv |
f7d494f61e544413a13e6ba1da2089cd adbbdbdc975ed84788366c52c1bb8d61 08b106dfeb12472e88207a069e15ba30 9ecfecc41421704f1ab4bde9f12751cf a8362fb718d10281b5ed76c2b2017eab 03b504eb2d992cccccc4c047b0f4663b 423290a5cb4c3d3ed2f193a6e819041b 5aa5c691a1ffe97abd12c2966efcb8d6 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio institucional Séneca |
| repository.mail.fl_str_mv |
adminrepositorio@uniandes.edu.co |
| _version_ |
1812133810638159872 |
| spelling |
Attribution-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Núñez Castro, Haydemar María05768730-2a33-411e-b3b1-2f3a105e0b76600Álvarez López, María Sofía689609b1-ed8f-494f-b3c1-83538435b87e600COMIT2023-07-25T20:45:17Z2023-07-25T20:45:17Z2023-07-16http://hdl.handle.net/1992/68749instname:Universidad de los Andesreponame:Repositorio Institucional Sénecarepourl:https://repositorio.uniandes.edu.co/Esta tesis desarrolla un árbol de decisión de redes neuronales convolucionales (CNNs), llamada GSpyNetTree, usada por la colaboración LIGO-Virgo para distinguir señales de ondas gravitacionales de ruido de detector.Tras la primera detección de ondas gravitacionales (GWs), las colaboraciones LIGO y Virgo iniciaron un nuevo campo de la astronomía al proporcionar una nueva forma de entender el Universo. A pesar de haber alcanzado sensibilidades capaces de detectar la amplitud extremadamente pequeña de las ondas gravitacionales, los datos de los detectores LIGO y Virgo contienen frecuentes ráfagas de ruido transitorio no Gaussiano, comúnmente conocidas como "glitches", que a menudo imitan o se solapan con las señales de ondas gravitacionales. Dada la mayor tasa de eventos de GWs esperada en el actual periodo de observación (O4), que comenzó en mayo de 2023, el proceso de selección de candidatos a GWs, incluida la identificación de glitches que se solapan con GWs o son morfológicamente similares a ellas, requiere una mayor automatización. Este trabajo desarrolla GSpyNetTree, el "Gravity Spy Convolutional Neural Network Decision Tree": un clasificador multi-CNN multi-etiqueta que identifica con precisión los "glitches" presentes en cada detector de GWs en el momento de una GW candidata. GSpyNetTree ha sido entrenada para ser robusta frente a una amplia gama de ruido de fondo, nuevas fuentes de "glitches" y la probable aparición de "glitches" y GWs solapados.La mayor parte de los aspectos computacionales de esta tesis fueron desarrollados usando recursos computacionales proveídos por el Laboratorio LIGO y apoyados por los Grants de la National Science Foundation de Estados Unidos PHY-0757058 y PHY-0823459.Ingeniero de Sistemas y ComputaciónPregradoMachine Learning49 páginasapplication/pdfspaUniversidad de los AndesIngeniería de Sistemas y ComputaciónFacultad de IngenieríaDepartamento de Ingeniería Sistemas y ComputaciónGSpyNetTree: Un clasificador de señales astrofísicas y ruido de detector para candidatos de ondas gravitacionales de LIGO-VirgoTrabajo de grado - Pregradoinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/acceptedVersionhttp://purl.org/coar/resource_type/c_7a1fTexthttp://purl.org/redcol/resource_type/TPMachine LearningOndas gravitacionalesLIGOConvolutional Neural NetworksCNNsIngenieríaB. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «Observation of Gravitational Waves from a Binary Black Hole Merger», Phys. Rev. Lett. 116, 061102 (2016).J. McIver y D. H. Shoemaker, «Discovering gravitational waves with Advanced LIGO», Contemporary Physics 61, 229-255 (2020).M. Zevin et al., «Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science», Classical and Quantum Gravity 34, arXiv:1611.04596 [astro-ph, physics:gr-qc, physics:physics], 064003 (2017).B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GW150914: The Advanced LIGO Detectors in the Era of First Discoveries», Phys. Rev. Lett. 116, 131103 (2016).B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs», en, 10.1103/PhysRevX.9.031040.B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration), «GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run», en, 10.1103/PhysRevX.11.021053.B. P. Abbott et al. (LIGO Scientific Collaboration and Virgo Collaboration and KAGRA Collaboration), «GWTC-3: Compact Binary Coalescences Observed by LIGO and Virgo During the Second Part of the Third Observing Run», en, 10.48550/arXiv.2111.03606.LIGO Scientific Collaboration and Virgo Collaboration, LIGO Laboratory at Caltech and MIT.P. R. Saulson, Fundamentals of interferometric gravitational wave detectors (World Scientific, 1994).The LIGO Scientific Collaboration, What is an interferometer?J. Harms, «Terrestrial Gravity Fluctuations», Living Reviews in Relativity 22, arXiv:1507.05850 [gr-qc], 6 (2019).The LIGO Scientific Collaboration and the Virgo Collaboration, «Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914», Classical and Quantum Gravity 33, arXiv:1602.03844 [astro-ph, physics:gr-qc, physics:physics], 134001 (2016).S. Anderson, K. Burtnyk y J. Kanner, LIGO-M1000066-v27: LIGO Data Management Plan, 2022.B. Berger, «Identification and mitigation of Advanced LIGO noise sources», Journal of Physics: Conference Series 957, 012004 (2018)D. Davis et al., «LIGO Detector Characterization in the Second and Third Observing Runs», Classical and Quantum Gravity 38, arXiv:2101.11673 [astro-ph, physics:gr-qc], 135014 (2021).J. S. Areeda et al., LigoDV-web: Providing easy, secure and universal access to a large distributed scientific data store for the LIGO Scientific Collaboration, arXiv:1611.01089 [astro-ph, physics:gr-qc], nov. de 2016.Jarov, S. et al., «A new method to distinguish gravitational-wave signals from detector glitches with Gravity Spy».S. Alvarez-Lopez et al., GSpyNetTree: A signal-vs-glitch classifier for gravitational-wave event candidates, 2023.A. Geron, Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : 2nd ed., https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/ (O'Reilly Media, Inc., Mumbai, 2020).A. C. Wilson et al., The Marginal Value of Adaptive Gradient Methods in Machine Learning, 2018.D. Masters y C. Luschi, Revisiting Small Batch Training for Deep Neural Networks, 2018.IBM, What are convolutional neural networks?LIGO Scientific Collaboration, LIGO Algorithm Library - LALSuite, free software (GPL), 2018.S. Husa et al., «Frequency-domain gravitational waves from nonprecessing black-hole binaries. I. New numerical waveforms and anatomy of the signal», Phys. Rev. D 93, 044006 (2016).S. Khan et al., «Frequency-domain gravitational waves from nonprecessing black-hole binaries. II. A phenomenological model for the advanced detector era», Phys. Rev. D 93, 044007 (2016).Pace A, Prestegard T, Moe B and Stephens B, «GraceDB Gravitational-Wave Candidate Event Database», https://gracedb.ligo.org/ (2020).The LIGO Scientific Collaboration and The Virgo Collaboration, «LIGO/Virgo Alert System (LVAlert)»M. Tan y Q. V. Le, EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks, 2020.C. Szegedy et al., «Rethinking the Inception Architecture for Computer Vision», en 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016), págs. 2818-2826.C. Szegedy et al., «Going deeper with convolutions», en 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), págs. 1-9.201729031PublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8799https://repositorio.uniandes.edu.co/bitstreams/b1e43b3b-200e-47da-8556-dfc231a0d387/downloadf7d494f61e544413a13e6ba1da2089cdMD52TEXTTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdf.txtTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdf.txtExtracted texttext/plain93565https://repositorio.uniandes.edu.co/bitstreams/a2d8ceb4-20d7-4ad2-9217-e3d07d4a5f94/downloadadbbdbdc975ed84788366c52c1bb8d61MD55Autorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdf.txtAutorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdf.txtExtracted texttext/plain1161https://repositorio.uniandes.edu.co/bitstreams/be2618da-8b06-4c79-95d4-c86b8ee8ab2d/download08b106dfeb12472e88207a069e15ba30MD57ORIGINALTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdfTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdfTrabajo de grado final aprobadoapplication/pdf40874871https://repositorio.uniandes.edu.co/bitstreams/d24034f0-d0ee-4b8c-b428-5360d5652ac9/download9ecfecc41421704f1ab4bde9f12751cfMD54Autorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdfAutorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdfHIDEapplication/pdf278273https://repositorio.uniandes.edu.co/bitstreams/fa8310c4-b4e7-4c0c-af4b-cf5d7e95389c/downloada8362fb718d10281b5ed76c2b2017eabMD53THUMBNAILTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdf.jpgTesis Ing Sistemas GSpyNetTree Maria Sofia Alvarez Lopez.pdf.jpgIM Thumbnailimage/jpeg6285https://repositorio.uniandes.edu.co/bitstreams/d8ba35c8-6d0f-42ed-adaa-d8a40a1cfb3c/download03b504eb2d992cccccc4c047b0f4663bMD56Autorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdf.jpgAutorizacion tesis Sistemas Maria Sofia Alvarez Lopez 201729031.pdf.jpgIM Thumbnailimage/jpeg15839https://repositorio.uniandes.edu.co/bitstreams/8e445e72-637b-4252-abb4-dc24995129bc/download423290a5cb4c3d3ed2f193a6e819041bMD58LICENSElicense.txtlicense.txttext/plain; charset=utf-81810https://repositorio.uniandes.edu.co/bitstreams/dfa904c6-6a9c-49dd-8167-a11ca9e75b6a/download5aa5c691a1ffe97abd12c2966efcb8d6MD511992/68749oai:repositorio.uniandes.edu.co:1992/687492023-10-10 15:23:20.025http://creativecommons.org/licenses/by-nd/4.0/open.accesshttps://repositorio.uniandes.edu.coRepositorio institucional Sénecaadminrepositorio@uniandes.edu.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 |