Complete list of publications.

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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. Astronomy & Astrophysics. 712, A31.
    Role: Lead and corresponding author. Developed the deep-learning framework and the associated doppleriann Python library, and conducted the analysis.
    Contribution to the field: Developed and validated a physically motivated deep-learning framework for recovering weak planetary Doppler signals from solar spectra. Injection–recovery experiments using HARPS-N observations demonstrated recovery of planetary signals down to 25 cm/s across orbital periods of 10–550 days, supporting progress towards Earth-mass planet detection.

  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.
    Role: Co-lead and corresponding author. Contributed to the methodological framework and manuscript preparation, developed the associated software, and 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.
    Role: Co-lead and corresponding author. Developed and implemented the methodology, conducted the data analysis, and created the associated software.

  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.
    Role: Lead and corresponding author. Developed and implemented the inference framework, conducted the data analysis, and created the associated neuralike Python library.
    Contribution to the field: Accelerated cosmological Bayesian inference by training neural networks on live points during nested sampling to approximate costly likelihoods without pretraining. Genetic algorithms guide the network architecture; tests across dark-energy models and datasets demonstrate the approach’s applicability.

  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.
    Role: Co-lead and corresponding author. Led the methodological development, implementation, and analysis, and created the associated software.

  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.
    Role: Lead author. Developed and implemented the methodology, conducted the analysis, and created the associated nnogada framework.
    Contribution to the field: Used genetic algorithms to optimise neural-network architectures for cosmological regression. Across distance-modulus reconstruction, quintessence inference, and photometric-redshift prediction, the optimised networks outperformed those selected by grid search.

  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.
    Role: Co-lead author. Contributed to manuscript writing and conceptual development, and advised the first author (PhD student).

  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.
    Role: Co-lead 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.
    Role: Lead author. Developed and implemented the reconstruction methodology and created the associated software.
    Contribution to the field: Developed a neural-network approach to reconstructing the Hubble parameter, growth rate, and supernova distance modulus from small datasets with limited theoretical assumptions. The study also explored variational autoencoders for generating synthetic covariance matrices.

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

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.
    Role: Contributed to Bayesian cosmological parameter estimation and to writing the Methods and Datasets sections.

  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.
    Role: Contributed to Sections 3.3 and 3.4 on reconstruction techniques and bioinspired algorithms, including the neural-network reconstruction results shown in Fig. 64.
    Contribution to the field: Synthesises observational tensions, systematic effects, proposed new physics, and emerging analysis methods into a roadmap for cosmology.

  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.
    Role: Corresponding author. Contributed to Bayesian cosmological parameter estimation and to writing the Methods and Datasets sections.

  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.
    Role: Reviewed the manuscript and contributed to methodological discussions of neural-network approaches for radial-velocity-based exoplanet detection.
    Contribution to the field: Demonstrated convolutional neural networks for modelling stellar activity from spectral-line profiles to improve sensitivity to weak planetary signals, with applications to Alpha Centauri B, Tau Ceti, and HARPS-N solar data.

  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.
    Role: Contributed to Bayesian cosmological parameter estimation and to writing the Methods and Datasets sections.

  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.
    Role: Contributed to Bayesian cosmological parameter estimation and to writing the Methods and Datasets sections.

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.
    Role: Lead author. Developed and implemented the method, wrote the manuscript, and presented the work at the conference.

  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.
    Role: Contributed to code development, manuscript writing, and manuscript review.

  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.
    Role: Contributed to code development, manuscript writing, and manuscript review, and advised the first author (undergraduate student) on the data-mining analysis.

  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.
    Role: Contributed to critical review of the manuscript and scientific discussions.

  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.
    Role: Lead author. Wrote and presented the work.

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.
    Role: Co-developed the associated IntroMCMC repository, produced plots, and contributed to manuscript writing and review and scientific discussions.

  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.
    Role: Contributed to manuscript review and scientific discussions, and supported the experimental design.

  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.
    Role: Lead and corresponding author. Developed the content and wrote the manuscript.

  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.
    Role: Lead author. Developed and implemented the augmented-reality application, available in the RAsolidsrev repository, and wrote the manuscript.

  5. Gómez Vargas, Isidro (2017). Posibilidad didáctica de la Realidad Aumentada. Sólo ensayo. Antología de jóvenes escritores Volumen II.
    Award: Second place, postgraduate category, Premio de Ensayo Innovación Educativa 2016, Instituto Politécnico Nacional.