Cosmology-informed neural networks to solve the background dynamics of the Universe

• Autores: Chantada, Augusto T.; Landau, Susana Judith; Protopapas, Pavlos; Scoccola, Claudia Graciela; Garraffo, Cecilia
• Año de publicación: 2023
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: alternative gravity theories; cosmological parameters; cosmology; dark energy
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

The field of machine learning has drawn increasing interest from various other fields due to the success of its methods at solving a plethora of different problems. An application of these has been to train artificial neural networks to solve differential equations without the need of a numerical solver. This particular application offers an alternative to conventional numerical methods, with advantages such as lower memory required to store solutions, parallelization, and, in some cases, a lower overall computational cost than its numerical counterparts. In this work, we train artificial neural networks to represent a bundle of solutions of the differential equations that govern the background dynamics of the Universe for four different models. The models we have chosen are ΛCDM, the Chevallier-Polarski-Linder parametric dark energy model, a quintessence model with an exponential potential, and the Hu-Sawicki f(R) model. We use the solutions that the networks provide to perform statistical analyses to estimate the values of each model's parameters with observational data; namely, estimates of the Hubble parameter from cosmic chronometers, type Ia supernovae data from the Pantheon compilation, and measurements from baryon acousstic oscillations. The results we obtain for all models match similar estimations done in the literature using numerical solvers. In addition, we estimate the error of the solutions that the trained networks provide by comparing them with the analytical solution when there is one, or to a high-precision numerical solution when there is not. Through these estimations we find that the error of the solutions is at most ∼1% in the region of the parameter space that concerns the 95% confidence regions that we find using the data, for all models and all statistical analyses performed in this work. Some of these results are made possible by improvements to the method of solving differential equations with artificial neural networks conceived in this work.
Fil: Chantada, Augusto T.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina
Fil: Landau, Susana Judith. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
Fil: Protopapas, Pavlos. Harvard University. School Of Engineering And Applied Sciences.; Estados Unidos
Fil: Scoccola, Claudia Graciela. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Garraffo, Cecilia. Harvard-Smithsonian Center for Astrophysics; Estados Unidos. Consejo Nacional de Investigaciónes Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Astronomía y Física del Espacio. - Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Astronomía y Física del Espacio; Argentina