Research

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

Publications

For a complete publication list, see All publications or the profiles below:

Selected Lead-Author and Co-Led Publications

For the complete list, see Led and co-led.

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.
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).
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.

Selected Collaborative Publications

For the complete list, see Collaborative.

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.
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.

Research software

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

doppleriann

Doppler-shift Inference with Artificial Neural Networks (DopplerIANN)

doppleriann is Python package for modeling Doppler shifts in high-resolution stellar spectra using physically motivated spectral-shell representations and deep learning. It contains the methodological framework presented in our paper: doi.org/10.1051/0004-6361/202659375.

Library GitHub repository: igomezv/doppleriann
Docs: doppleriann/Docs

Figura Figura

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

Figura

Projects using nnogada

neuralike

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

Presentations

Selected invited talks, conference presentations, and seminars. Complete list, including posters, 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].

Scientific service

Journal peer review

Review and Evaluation Activities

Conference reviewer: MICAI 2026, COMIA 2026.
Book reviewer: CRC Press (2025).
Grant evaluator:: Internal Research Grants Programme, University of Malta (2025), and CONACYT postdoctoral grants (2023).