1 code implementation • 18 May 2023 • Niall Jeffrey, Benjamin D. Wandelt
Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function.
no code implementations • 4 Feb 2022 • Pablo Lemos, Niall Jeffrey, Miles Cranmer, Shirley Ho, Peter Battaglia
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.
no code implementations • 14 Jan 2022 • Benjamin Remy, Francois Lanusse, Niall Jeffrey, Jia Liu, Jean-Luc Starck, Ken Osato, Tim Schrabback
We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior.
2 code implementations • 11 Nov 2020 • Niall Jeffrey, Benjamin D. Wandelt
High-dimensional probability density estimation for inference suffers from the "curse of dimensionality".
3 code implementations • 17 Sep 2020 • Niall Jeffrey, Justin Alsing, Francois Lanusse
We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
2 code implementations • 1 Aug 2019 • Niall Jeffrey, François Lanusse, Ofer Lahav, Jean-Luc Starck
With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent.
Cosmology and Nongalactic Astrophysics
1 code implementation • 5 Oct 2018 • Niall Jeffrey, Alan F. Heavens, Philip D. Fortio
We use Dataflow Engines (DFE) to construct an efficient Wiener filter of noisy and incomplete image data, and to quickly draw probabilistic samples of the compatible true underlying images from the Wiener posterior.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics