123 citations · 514 across the 42 of their papers we have counts for
9 papers · 1 filter
syren-new: Precise formulae for the linear and nonlinear matter power spectra with massive neutrinos and dynamical dark energy
Ce Sui, Deaglan J. Bartlett, Shivam Pandey +3
Current and future large scale structure surveys aim to constrain the neutrino mass and the equation of state of dark energy. We aim to construct accurate and interpretable symboli…
CHARM: Creating Halos with Auto-Regressive Multi-stage networks
Shivam Pandey, Chirag Modi, Benjamin D. Wandelt +7
To maximize the amount of information extracted from cosmological datasets, simulations that accurately represent these observations are necessary. However, traditional simulations…
COmoving Computer Acceleration (COCA): -body simulations in an emulated frame of reference
Deaglan J. Bartlett, Marco Chiarenza, Ludvig Doeser +1
-body simulations are computationally expensive, so machine-learning (ML)-based emulation techniques have emerged as a way to increase their speed. Although fast, surrogate mode…
Scant evidence for thawing quintessence
William J. Wolf, Carlos García-García, Deaglan J. Bartlett +1
New constraints on the expansion rate of the Universe seem to favor evolving dark energy in the form of thawing quintessence models, i.e., models for which a canonical, minimally c…
Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
Andrei Constantin, Deaglan Bartlett, Harry Desmond +1
Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena, physical equations exhibit structural…
Bye-bye, Local-in-matter-density Bias: The Statistics of the Halo Field Are Poorly Determined by the Local Mass Density
Deaglan J. Bartlett, Matthew Ho, Benjamin D. Wandelt
Bias models relating the dark matter field to the spatial distribution of halos are widely used in current cosmological analyses. Many models predict halos purely from the local Eu…
The Inefficiency of Genetic Programming for Symbolic Regression
Gabriel Kronberger, Fabricio Olivetti de Franca, Harry Desmond +2
We analyse the search behaviour of genetic programming for symbolic regression in practically relevant but limited settings, allowing exhaustive enumeration of all solutions. This…
syren-halofit: A fast, interpretable, high-precision formula for the CDM nonlinear matter power spectrum
Deaglan J. Bartlett, Benjamin D. Wandelt, Matteo Zennaro +2
Rapid and accurate evaluation of the nonlinear matter power spectrum, , as a function of cosmological parameters and redshift is of fundamental importance in cosmology. Analy…
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
Matthew Ho, Deaglan J. Bartlett, Nicolas Chartier +12
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) inferenc…