Research

Research focused on the intersection of deep learning, Bayesian inference, and astrophysics, with applications to cosmology, exoplanets, and stellar activity.

Selected publications
Research software
Scientific service
Selected presentations

Publications

Selected publications. For a complete publication list, see here or the following profiles:

Lead and co-lead

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 author. Co-developed the methodological framework and manuscript preparation; developed the associated code repository; supervised the first author (PhD student); corresponding author.
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.
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.
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.
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.
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.
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. Co-developed the methodological framework, implementation, and analysis; supervised the first author (PhD student).

Collaborations

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 the sections on reconstruction techniques and bioinspired algorithms for model selection, including material related to neural-network reconstruction.
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 exoplanet detection.
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: Corresponding author. Led the Bayesian statistical analysis and contributed to manuscript preparation and scientific review.

Research software

Additional projects and software repositories are available on my GitHub profile.

SimpleMC

Cosmological parameter inference toolkit for Bayesian analysis and statistical sampling.

SimpleMC is a cosmological parameter estimation framework originally developed by Dr. A. Slosar and Dr. J. A. Vázquez. Between 2019 and 2023, I contributed to the development and maintenance of the codebase, including nested sampling implementations, convergence criteria for Metropolis–Hastings algorithms, post-processing utilities, and additional analysis modules.

Links

Library GitHub repository: ja-vazquez/SimpleMC
Documentation: igomezv/SimpleMC/Docs
Workshop/tutorial: igomezv/simplemc_workshop

Figura

nnogada

Neural networks optimized with genetic algorithms for data-driven inference and reconstruction.

nnogada (Neural Networks Optimized by Genetic Algorithms in Data Analysis) is a framework combining neural networks and genetic algorithms for flexible modeling, reconstruction, and parameter inference in astrophysical and cosmological applications.

Links

Library GitHub repository: igomezv/nnogada
Documentation: docs/nnogada

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Projects using nnogada

neuralike

Surrogate-assisted Bayesian inference for accelerating cosmological likelihood evaluations.

neuralike implements deep-learning surrogate models combined with genetic-algorithm optimization to accelerate Bayesian inference workflows in cosmology, particularly for computationally expensive likelihood evaluations within sampling pipelines.

Links

Library GitHub repository: igomezv/neuralike
Integration with SimpleMC and nested sampling using dynesty: igomezv/simplemc_tests

Figura

ANN Reconstructions

Neural-network cosmological reconstructions with uncertainty quantification.

Collection of Python implementations for model-independent reconstruction of cosmological observables using artificial neural networks, Monte Carlo Dropout uncertainty estimation, and hyperparameter optimization techniques.

Repositories

Unified reconstruction library: igomezv/alp
Galaxy rotation curves: igomezv/Reconstructing-RC-with-ANN
LSST supernova simulations: igomezv/LSST_DE_neural_reconstruction
Cosmological observables reconstruction: igomezv/neuralCosmoReconstruction

NCosmoVAE

Variational autoencoders for generative modeling of cosmological N-body simulations.

NCosmoVAE is a variational autoencoder framework trained on cosmological N-body simulations to generate realistic dark matter halo realizations. The project incorporates hyperparameter optimization using genetic algorithms with the optuna Python library.

Links

Library repository: igomezv/NcosmoVAE

Figura

Scientific service

Journal peer review

Editorial and evaluation activities

Reviewer for COMIA 2026.
Reviewer for CRC Press (2025).
External evaluator for the Internal Research Grants Programme, University of Malta (2025).
Reviewer for CONACYT postdoctoral grants (2023).

Selected presentations

Selected invited talks, conference presentations, and seminars. Complete list: here.

2026
- [Seminar] Deep learning for small astrophysical datasets: from cosmology to exoplanet detection. Institutional seminar, Instituto de Astrofísica de Andalucía (IAA-CSIC), Granada, Spain. [On-site].
2025
- [Conference] Deep Learning strategies for detecting Earth-size exoplanets in HARPS-N stellar spectra., EPSC-DPS 2025, Helsinki, Finland. [On-site].
- [Conference] Reaching the 10 cm/s planetary detection limit on HARPS-N solar data using deep learning. The Sixth Workshop on Extremely Precise Radial Velocities (EPRV 6), Porto, Portugal. [On-site].
- [Seminar] Machine learning for small astrophysical datasets: applications in cosmology and exoplanets. Pizza Seminar, Instituto de Ciencias del Espacio (ICE-CSIC), Barcelona, Spain. [On-site].
- [Conference] Deep learning for small astrophysical datasets: applications in cosmology and exoplanets. ICGCAS-2025, PICS, Odisha, India. [Online].
2024
- [Seminar] Machine Learning for Astrophysical Data Analysis and Stellar Spectra Modeling, Exoplanet Group Seminar, University of Geneva, Geneva, Switzerland. [On-site].
- [Talk] Aceleración de la Inferencia Bayesiana mediante Redes Neuronales y Algoritmos Genéticos, III Mini Workshop on HPC in Science and Engineering, ICF-UNAM, Cuernavaca, México. [Online].