Complete list of articles.

Research Articles

Led and Co-Led

  1. Gómez-Vargas, I., Dumusque, X., Zhao, Y., Al Moulla, K. & Cretignier, M. (2026). Modeling Doppler Shifts in radial-velocity data with deep learning toward Earth-mass exoplanet detection. Accepted in Astronomy & Astrophysics. arXiv:2606.18464.
    Contribution: Lead and corresponding author. Developed the associated code library.

  2. Chacón-Lavanderos, J., Gómez-Vargas, I., Menchaca-Mendez, R., & Vázquez, J. A. (2026). Variational autoencoder for generating realistic N-body simulations for dark matter halos. Physical Review D. 113(6), 063520.
    Contribution: Co-lead and corresponding author. Co-developed the methodological framework and manuscript preparation; developed the associated code repository; supervised the first author (PhD student).

  3. Garcia-Arroyo, G., Gómez-Vargas, I., & Vázquez, J. A. (2026). Data-driven modeling of rotation curves with artificial neural networks. Physics of the Dark Universe, 52, 102240.
    Contribution: Co-lead author and corresponding author. Developed the methodology, implementation, and data analysis; developed the associated code repository.

  4. Gómez-Vargas, I., & Vázquez, J. A. (2024). Deep learning and genetic algorithms for cosmological Bayesian inference speed-up. Physical Review D. 110(8), 083518.
    Contribution: Lead author. Developed the inference framework, methodology, implementation, and data analysis; developed the associated code repository.

  5. 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. Physics of the Dark Universe, 46, 101706.
    Contribution: Co-lead author and corresponding author. Led the methodological development, implementation, and analysis; developed the associated code repository.

  6. Gómez-Vargas, I., Andrade, J. B., & Vázquez, J. A. (2023). Neural networks optimized by genetic algorithms in cosmology. Physical Review D. 107(4), 043509.
    Contribution: Lead author. Developed the methodology, implementation, and analysis; developed the associated code repository.

  7. Medel Esquivel, R., Gómez-Vargas, I., Morales Sánchez, A. A., García-Salcedo, R., & Vázquez, J. A. (2023). Cosmological parameter estimation with Genetic Algorithms. Universe, 10(1), 11.
    Contribution: Co-lead author. Writing, conceptual development, and advising the PhD student (first author).

  8. 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.
    Contribution: Co-lead author and corresponding author. Co-led the methodological development, implementation, and analysis.

  9. 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.
    Contribution: Lead author. Developed the reconstruction methodology and implementation; developed the associated code repository.

  10. Rojas Olvera, J. de Dios, Gómez-Vargas, I., & Vázquez, J. A. (2022). Observational Cosmology with Artificial Neural Networks. Universe, 8(2), 120.
    Contribution: Co-lead author. Writing, conceptual development, and advising the undergraduate student (first author).

Collaborative

  1. Chaudhary, H., Capozziello, S., Sharma, V. K., Gómez-Vargas, I., & Mustafa, G. (2026). Evidence for evolving dark energy from DESI DR2 BAO and Pantheon+, DES-Dovekie, and Union3. European Physical Journal C, 86, 564.
    Contribution: Contributed to the Bayesian cosmological parameter estimation.

  2. Di Valentino, E., et al. (including Gómez-Vargas, I.) (2025). The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics. Physics of the Dark Universe, 101965.
    Contribution: Contributed to Sections 3.3 and 3.4 on reconstruction techniques and bioinspired algorithms, including the neural-network reconstruction results shown in Fig. 64.

  3. Tamayo, D., Urquilla, E., & Gómez-Vargas, I. (2025). Equivalence of Dark Energy Models: A Theoretical and Bayesian Perspective. Physics of the Dark Universe, 48, 101901.
    Contribution: Contributed to the Bayesian cosmological parameter estimation.

  4. 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.
    Contribution: Contributed to manuscript review and methodological discussion of neural-network approaches for radial-velocity-based exoplanet detection.

  5. Vázquez, J. A., Tamayo, D., Garcia-Arroyo, G., Gómez-Vargas, I., Quiros, I., & Sen, A. A. (2024). Coupled Multi Scalar Field Dark Energy. Physical Review D, 109(2), 023511.
    Contribution: Contributed to the Bayesian cosmological parameter estimation.

  6. Garcia-Salcedo, R., Gómez-Vargas, I., Gonzalez, 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.
    Contribution: Contributed to the Bayesian cosmological parameter estimation.

Conference Proceedings

  1. Gómez-Vargas, I., Esquivel, R. M., García-Salcedo, R., & Vázquez, J. A. (2021). Neural network within a Bayesian inference framework. Journal of Physics: Conference Series, 1723(1), 012022.

  2. 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. Journal of Physics: Conference Series, 1723(1), 012021.

  3. 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. Journal of Physics: Conference Series, 1723(1), 012034.

  4. Medel-Esquivel, R., Gómez-Vargas, I., Rivera-Montalvo, T., & Salcedo, R. G. (2019). Cosmological evolution for magnetic universe based in a simple nonlinear electrodynamics. Journal of Physics: Conference Series, 1221(1), 012038.

  5. Gómez-Vargas, I., Medel-Esquivel, R., & García-Salcedo, R. (2019). Cosmic voids, spatial algorithms and data structures. Journal of Physics: Conference Series, 1221(1), 012031.

Outreach, Education, and Science Communication

  1. Medel Esquivel, R., Gómez-Vargas, I., Vázquez, J. A., & Salcedo, R. G. (2021). An introduction to Markov chain Monte Carlo. Boletín de Estadística e Investigación Operativa, 37(1), 47–74.

  2. Medel-Esquivel, R., Gómez-Vargas, I., García-Salcedo, R., & Vázquez, J. A. (2021). A Simple Estimation of the Size of Carbon Atoms Using a Pencil Lead. The Physics Teacher, 59(6), 480–481.

  3. Gómez-Vargas, I., Medel-Esquivel, R., Vázquez, J. A., & García-Salcedo, R. (2019). Una aplicación de las redes neuronales en la cosmología. Komputer Sapiens, outreach journal of the Sociedad Mexicana de Inteligencia Artificial.

  4. Gómez-Vargas, I., Medel Esquivel, R. M., & García-Salcedo, R. (2018). Realidad Aumentada como herramienta didáctica en geometría 3D. Latin-American Journal of Physics Education, 12(4), 3.

  5. Gómez Vargas, Isidro (2017). Posibilidad didáctica de la Realidad Aumentada. Sólo ensayo. Antología de jóvenes escritores Volumen II.