1 code implementation • 27 Feb 2024 • Deaglan J. Bartlett, Benjamin D. Wandelt, Matteo Zennaro, Pedro G. Ferreira, Harry Desmond
Our symbolic expressions for $k_\sigma$, $n_{\rm eff}$ and $C$ have root mean squared fractional errors of 0. 8%, 0. 2% and 0. 3%, respectively, for redshifts below 3 and a wide range of cosmologies.
1 code implementation • 6 Feb 2024 • Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier, Carolina Cuesta-Lazaro, Simon Ding, Axel Lapel, Pablo Lemos, Christopher C. Lovell, T. Lucas Makinen, Chirag Modi, Viraj Pandya, Shivam Pandey, Lucia A. Perez, Benjamin Wandelt, Greg L. Bryan
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
1 code implementation • 27 Nov 2023 • Deaglan J. Bartlett, Lukas Kammerer, Gabriel Kronberger, Harry Desmond, Pedro G. Ferreira, Benjamin D. Wandelt, Bogdan Burlacu, David Alonso, Matteo Zennaro
We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0. 2% between $k = 9\times10^{-3} - 9 \, h{\rm \, Mpc^{-1}}$ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression.
no code implementations • 25 Oct 2023 • Tomás Sousa, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
Inflation is a highly favoured theory for the early Universe.
1 code implementation • 13 Apr 2023 • Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
In this paper we develop methods to incorporate detailed prior information on both functions and their parameters into SR. Our prior on the structure of a function is based on a $n$-gram language model, which is sensitive to the arrangement of operators relative to one another in addition to the frequency of occurrence of each operator.
1 code implementation • 11 Jan 2023 • Harry Desmond, Deaglan J. Bartlett, Pedro G. Ferreira
We apply a new method for learning equations from data -- Exhaustive Symbolic Regression (ESR) -- to late-type galaxy dynamics as encapsulated in the radial acceleration relation (RAR).
6 code implementations • 21 Nov 2022 • Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira
To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations -- made with a given basis set of operators and up to a specified maximum complexity -- and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints.
no code implementations • 2 Jul 2020 • Deaglan J. Bartlett, Harry Desmond, Julien Devriendt, Pedro G. Ferreira, Adrianne Slyz
We study the displacements between the centres of galaxies and their supermassive black holes (BHs) in the cosmological hydrodynamical simulation Horizon-AGN, and in a variety of observations from the literature.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics