• Papers.
• Academic presentations.
• Peer reviews.

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Papers

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• Research profiles: Google Scholar, ResearchGate, ADS, arXiv.
• Research.
• Proceedings.
• Outreach.

Research

• Gómez-Vargas, I., & Vázquez, J. A. (2024). Deep Learning and genetic algorithms for cosmological Bayesian inference speed-up. Physical Review D. 110(8) 083518.

• Zhao, Y., Dumusque, X., Cretignier, M., Cameron, A.C., Latham, D.W., López-Morales, M., Mayor, M., Sozzetti, A., Cosentino, R., Gómez-Vargas, I., Pepe, F., & Udry, S. (2024). Improving Earth-like planet detection in radial velocity using deep learning. Astronomy & Astrophysics. 687, A281.

• Mitra, A., Gómez-Vargas, I., & Zarikas, V. (2024).Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory. arXiv:2402.18124v1. Accepted in Physics of the Dark Universe.

• García-Salcedo, R., Gómez-Vargas, I., González, T., Martinez-Badenes, V., & Quiros, I. (2024). Combined Studies Approach to Rule Out Cosmological Models Which Are Based on Nonlinear Electrodynamics. Universe, 10(9), 353.

• Garcia-Arroyo, G., Gómez-Vargas, I., & Vázquez, J. A. (2024). Reconstructing rotation curves with artificial neural networks. arXiv:2404.05833v1.

• Vázquez, J. A., Tamayo, D., Garcia-Arroyo, G., Gómez-Vargas, I., Quiros, I. & Sen, Anjan A. (2024). Coupled Multi Scalar Field Dark Energy. Physical Review D. 109(2) 023511.

• Gómez-Vargas, I., Vázquez, J. A., Esquivel, R. M., & García-Salcedo, R. (2023). Neural Network Reconstructions for the Hubble Parameter, Growth Rate and Distance Modulus. European Physical Journal C. 83(4). 304.

• Gómez-Vargas, I., Andrade, J. B., & Vázquez, J. A. (2023). Neural Networks Optimized by Genetic Algorithms in Cosmology. Physical Review D. 107(4). 043509.

• Medel Esquivel, R., Gómez-Vargas, I., Morales Sánchez, AA., García-Salcedo, R. & Vázquez, J. A. (2023). Cosmological parameter estimation with Genetic Algorithms. Universe. 10(1) 11.

• Chacón, J., Gómez-Vargas, I., Menchaca Mendez, R., & Vázquez, J. A. (2023). Analysis of Dark Matter Halo Structure Formation in N-body Simulations with Machine Learning. Physical Review D. 107(12). 123515.

• Rojas Olvera, J. de Dios , Gómez-Vargas, I., & Vázquez, J. A. (2022). Observational Cosmology with Artificial Neural Networks. Universe, 8(2), 120.

Proceedings

• Gómez-Vargas, I., Esquivel, R. M., García-Salcedo, R., & Vázquez, J. A. (2021). Neural network within a bayesian inference framework. In Journal of Physics: Conference Series (Vol. 1723, No. 1, p. 012022). IOP Publishing.

• Medel-Esquivel, R., Gómez-Vargas, I., Montalvo, T. R., Vázquez, J. A., & García-Salcedo, R. (2021). The inverse problem of a dynamical system solved with genetic algorithms. In Journal of Physics: Conference Series (Vol. 1723, No. 1, p. 012021). IOP Publishing.

• Toledo, M. R., Vázquez, E. R., García-Salcedo, R., Gómez-Vargas, I., Uruchurtu, E. S., & Rivera-Montalvo, T. (2021). Data Mining applied to interventional cardiology procedures. In Journal of Physics: Conference Series (Vol. 1723, No. 1, p. 012034). IOP Publishing.

• Vazquez, JA, Gómez-Vargas, I & Medel-Esquivel, R. (2019) Cosmología observacional con Redes Neuronales Artificiales. Proceedings of the UNAM Physics Summer School.

• Medel-Esquivel, R., Gómez-Vargas, I., Rivera-Moltalvo, T., & Salcedo, R. G. (2019, June). Cosmological evolution for magnetic universe based in a simple nonlinear electrodynamics. In Journal of Physics: Conference Series (Vol. 1221, No. 1, p. 012038). IOP Publishing.

• Gómez-Vargas, I., Medel-Esquivel, R., & García-Salcedo, R. (2019, June). Cosmic voids, spatial algorithms and data structures. In Journal of Physics: Conference Series (Vol. 1221, No. 1, p. 012031). IOP Publishing.

Educational and outreach

• Check list in the outreach and didactic papers section.

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Academic presentations

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[Last 5 years]

• 2024
  • [Flash talk/poster] Deep Learning strategies to planet detection using HARPS-N stellar spectra, Junior Research Assembly (JURA) V. PlanetS, Murten, Switzerland.
  • [Seminar] Aprendizaje automático en la inferencia estadística de datos cosmológicos, Seminario de Cosmología y Astrofísica Relativista. Physics department, CINVESTAV.
  • [Talk] Aceleración de la Inferencia Bayesiana mediante Redes Neuronales y Algoritmos Genéticos, III Mini Workshop on HPC in Science and Engineering, ICF-UNAM, Cuernavaca, Morelos, México.
  • [Seminar] Machine Learning for Astrophysical Data Analysis and Stellar Spectra Modeling, Exoplanet Group Seminar. University of Geneva, Geneva, Switzerland.
• 2023
  • [Seminar] Aprendizaje automático en el análisis de datos cosmológicos, Seminario del grupo de gravitación y cosmología del Instituto de Ciencias Físicas, ICF-UNAM, Cuernavaca, Morelos.
  • [Poster] Neural networks in cosmological data analysis, Addressing observational tensions in cosmology with systematics and fundamental physics (CosmoVerse). Faculdade de Ciências da Universidade de Lisboa. Lisboa, Portugal.
• 2022
  • [Poster] Neural nets in cosmological analysis, VIII Essential Cosmology for the Next Generation, Playa del Carmen, Quintana Roo.
  • [Conference] Análisis de datos cosmológicos con redes neuronales, Mini Workshop on High Performance Computing in Science and Engineering, Universidad Autónoma de Hidalgo, Pachuca, Hidalgo.
  • [Poster] Dark matter halo analysis on N-body cosmological simulations with deep learning, LXV Congreso Nacional de Física, Universidad de Zacatecas, México.
  • [Poster] Neural networks in cosmological data analysis, 25th annual International Conference on Particle Physics and Cosmology (COSMO’22), Rio de Janeiro Planetarium, Brazil.
• 2021
  • [Talk] SimpleMC y redes neuronales para modelos de energía oscura, COSMO Meeting III, ICF-UNAM, Cuernavaca, Morelos.
  • [Seminar] Inferencia Bayesiana y redes neuronales para modelos de energía oscura, Seminario de Teorías de Gravedad y Señales Observacionales, Universidad de Guanajuato.
  • [Seminar] Redes Neuronales: aplicaciones estadísticas en Cosmología Observacional, Seminario del grupo de gravitación y cosmología del Instituto de Ciencias Físicas, ICF-UNAM, Cuernavaca, Morelos.
  • [Poster] Neural network training within a Bayesian inference framework, X International Congress of Physics Engineering, UAM-Azcapotzalco, CDMX.
  • [Poster] The inverse problem of a dynamical system solved with genetic algorithms, X International Congress of Physics Engineering, UAM-Azcapotzalco, CDMX.
  • [Poster] Data Mining applied to interventional cardiology procedures, X International Congress of Physics Engineering, UAM-Azcapotzalco, CDMX.
• 2020
  • [Seminar] Muestreo anidado, redes neuronales y SimpleMC, Seminario del grupo de gravitación y cosmología del Instituto de Ciencias Físicas, ICF-UNAM, Cuernavaca, Morelos.
  • [Talk] Métodos estadísticos con redes neuronales en cosmología observacional, V Encuentro de Modelado Matemático en Física y Geometría, BUAP, MCTP.
  • [Seminar] Redes Neuronales aplicadas a métodos estadísticos de la Cosmología Observacional, Seminario de estudiantes de cosmología, Cuernavaca, Morelos.

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Peer reviews

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Peer reviews validated by Web of science:

• 3 Physical Review D. Private info.
• 1 IEEE Transactions on Pattern Analysis and Machine Intelligence. Private info.
• 1 Physics of the Dark Universe. Private info.
• 1 Indian Journal of Physics. Private info.
• 1 Ciencia ergo-sum. Private info.
• 1 Frontiers in Public Health. Public info.