Our missionWe are an international collaboration of researchers interested in developing and applying cutting-edge statistical inference techniques to study the spatial distribution of matter in our Universe. We embrace the latest innovations in information theory and artificial intelligence to optimally extract physical information from data and use derived results to facilitate new discoveries.
Our latest results
Testing gravity with the positions of supermassive black holes
A key frontier in cosmology is testing the nature of gravity. We use constrained simulations of local structure based on the BORG algorithm to map out the scalar field in galileon gravity, a leading competitor to General Relativity. From this we predict the behaviour of supermassive black holes in galaxies, and then compare with observational data to constrain the coupling strength of the galileon.
Simulating the Universe on a mobile phone
Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution. In this post, we discuss a new, perfectly parallel algorithm to simulate the Universe on a variety of hardware architectures.
Why neural networks don’t work and how to use them
Throughout the scientific community neural networks are being used for a variety of different tasks. Unfortunately, this is normally done without thought of the statistical implication. Here we lay down the statistical notions showing why neural networks cannot be used by themselves for scientific purposes. We then provide a suite of methods which allows them to be used safely within a statistical framework for parameter inference.