2023/05/24 by Konstantinos F. Dialektopoulos, Dialektopoulos, Konstantinos F., Purba Mukherjee +5 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Artificial neural network #Astrophysics #Bayesian probability #Computational Physics and Python Applications #Computer science #Context (archaeology) #Cosmology #Cosmology and Gravitation Theories #FOS: Physical sciences #General Relativity and Quantum Cosmology (gr-qc) #Geometry #Machine learning #Markov chain Monte Carlo #Mathematics #Particle physics theoretical and experimental studies #Physics #Scalar (mathematics) #Statistical physics #Tensor (intrinsic definition)
paper · pdf · doi:10.48550/arxiv.2305.15500
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/05/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural networks have shown great promise in providing a data-first approach to exploring new physics. In this work, we use the full implementation of late time cosmological data to reconstruct a number of scalar-tensor cosmological models within the context of neural network systems. In this pipeline, we incorporate covariances in the data in the neural network training algorithm, rather than a likelihood which is the approach taken in Markov chain Monte Carlo analyses. For general subclasses of classic scalar-tensor models, we find stricter bounds on functional models which may help in the understanding of which models are observationally viable.