Faster Bayesian inference with neural network bundles and new results for f ( R ) models

• Autores: Chantada, Augusto Tomás; Landau, Susana Judith; Protopapas, Pavlos; Scoccola, Claudia Graciela; Garraffo, Cecilia
• Año de publicación: 2024
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: cosmology; neural networks; bayesian inference
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

In the last few years, there has been significant progress in the development of machine learning methods tailored to astrophysics and cosmology. We have recently applied one of these, namely, the neural network bundle method, to the cosmological scenario. Moreover, we showed that in some cases the computational times of the Bayesian inference process can be reduced. In this paper, we present an improvement to the neural network bundle method that results in a significant reduction of the computational times of the statistical analysis. The novel aspect consists of the use of the neural network bundle method to calculate the luminosity distance of type Ia supernovae, which is usually computed through an integral with numerical methods. In this work, we have applied this improvement to the Hu-Sawicki and Starobinsky f (R ) models. We also performed a statistical analysis with data from type Ia supernovae of the Pantheon + compilation and cosmic chronometers. Another original aspect of this work is the different treatment we provide for the absolute magnitude of type Ia supernovae during the inference process, which results in different estimates of the distortion parameter than the ones obtained in the literature. We show that the statistical analyses carried out with our new method require lower computational times than the ones performed with both the numerical and the neural network method from our previous work. This reduction in time is more significant in the case of a difficult computational problem such as the ones addressed in this work.
Fil: Chantada, Augusto Tomás. 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. 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. John A. Paulson School Of Engineering & Applied Sciences ; Harvard University;
Fil: Scoccola, Claudia Graciela. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina
Fil: Garraffo, Cecilia. Harvard-Smithsonian Center for Astrophysics; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina