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Systematic Review

A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology

1
Facultad de Ingeniería, Universidad San Sebastián, Bellavista 7, Santiago 8420524, Chile
2
Departamento de Sistemas de Información, Universidad del Bío-Bío, Avenida Andrés Bello 720, Chillán 3800708, Chile
3
Departamento de Física, Universidad Católica del Norte, Avenida Angamos 0610, Casilla 1280, Antofagasta 1270709, Chile
4
Facultad de Ciencias, Universidad San Sebastián, Lago Panguipulli 1390, Puerto Montt 5501842, Chile
5
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Galaxies 2025, 13(5), 114; https://doi.org/10.3390/galaxies13050114
Submission received: 20 May 2025 / Revised: 12 September 2025 / Accepted: 26 September 2025 / Published: 9 October 2025

Abstract

This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify effective methodologies, highlight gaps, and propose future research directions. Our review identifies several key findings: (1) Various machine learning techniques, including Bayesian neural networks, Gaussian processes, and deep learning models, have been applied to cosmological data analysis, improving parameter estimation and handling large datasets. However, models achieving significant computational speedups often exhibit worse confidence regions compared to traditional methods, emphasizing the need for future research to enhance both efficiency and measurement precision. (2) Traditional cosmological methods, such as those using Type Ia Supernovae, baryon acoustic oscillations, and cosmic microwave background data, remain fundamental, but most studies focus narrowly on specific datasets. We recommend broader dataset usage to fully validate alternative cosmological models. (3) The reviewed studies mainly address the H0 tension, leaving other cosmological challenges—such as the cosmological constant problem, warm dark matter, phantom dark energy, and others—unexplored. (4) Hybrid methodologies combining machine learning with Markov chain Monte Carlo offer promising results, particularly when machine learning techniques are used to solve differential equations, such as Einstein Boltzmann solvers, prior to Markov chain Monte Carlo models, accelerating computations while maintaining precision. (5) There is a significant need for standardized evaluation criteria and methodologies, as variability in training processes and experimental setups complicates result comparability and reproducibility. (6) Our findings confirm that deep learning models outperform traditional machine learning methods for complex, high-dimensional datasets, underscoring the importance of clear guidelines to determine when the added complexity of learning models is warranted.
Keywords: systematic literature review; machine learning; deep learning; cosmology; observational constraints systematic literature review; machine learning; deep learning; cosmology; observational constraints

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MDPI and ACS Style

Rojas, L.; Espinoza, S.; González, E.; Maldonado, C.; Luo, F. A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology. Galaxies 2025, 13, 114. https://doi.org/10.3390/galaxies13050114

AMA Style

Rojas L, Espinoza S, González E, Maldonado C, Luo F. A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology. Galaxies. 2025; 13(5):114. https://doi.org/10.3390/galaxies13050114

Chicago/Turabian Style

Rojas, Luis, Sebastián Espinoza, Esteban González, Carlos Maldonado, and Fei Luo. 2025. "A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology" Galaxies 13, no. 5: 114. https://doi.org/10.3390/galaxies13050114

APA Style

Rojas, L., Espinoza, S., González, E., Maldonado, C., & Luo, F. (2025). A Systematic Literature Review of Machine Learning Techniques for Observational Constraints in Cosmology. Galaxies, 13(5), 114. https://doi.org/10.3390/galaxies13050114

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