Joint analysis of galaxy clustering and weak lensing via simulation-based inference

Physical cosmology is dominated by a successful standard model connecting high-energy physics to observational astronomy, in which the Universe evolves from the "Big Bang" to its present state. However, we currently do not know the cause of the accelerated expansion and the masses of neutrino particles. Precise measurements of the acceleration of expansion will test the validity of general relativity and shed light on the physical nature of dark energy, an unknown form of energy that affects the Universe on the largest scales. Measuring the unknown masses of neutrinos, long thought to be zero, is an important step towards elucidating their essential nature and exploring new physics beyond the standard model of particle physics. This research programme tackles these two enthralling problems.
Answering such physical questions requires extracting information from large astronomical data sets with sufficient accuracy. This is a complex problem, since changes to observables when one changes the model away from the standard model are extremely subtle. Advanced techniques are required to tease out the physics. However, existing methods rely on various simplifications and assumptions that at some level do not hold. For the next generation of galaxy surveys, it will be vital to improve upon these.
As a response, my techniques represent a radical rethinking of how to analyse data, incorporating of the laws of physics and the working of instruments into computer models used within data analysis. This has only been possible to do at all in the last few years thanks to novel methods that I introduced, including statistical algorithms that drastically reduce the number of model evaluations required, and computational physics techniques that make these evaluations massively parallel. These developments form the basis of the principled methodology that I will follow, known as 'simulation-based inference'.
Simulation-based inference using physical computer models will uniquely enable me, for the first time, to exploit complete observed maps of the large-scale structure of the Universe and, thus, to go beyond the results obtained with standard estimators of so-called 'correlation functions'. Consequently, the key innovation that will allow me to address the science goals is a simultaneous analysis of the two main effects observable in large-scale structure surveys: galaxy clustering and weak gravitational lensing. This development is particularly timely and significant in the context of the large-scale observational project of ESA's Euclid satellite, as it will provide unique insights into both aspects of its core programme. The use of physical computer models to jointly analyse galaxy clustering and weak gravitational lensing will reduce statistical and systematic uncertainties, increase the precision of cosmological results, and improve their accuracy with respect to standard methods based on correlation functions.
My proposal consists of developing, validating, and applying new physical computer data models for galaxy survey data analysis, in order to address the two scientific objectives. This research constitutes a conceptually entirely new approach to extracting physical information from large-scale astronomical data. The proposed work will culminate with the first joint clustering-lensing analyses of synthetic, then real Euclid data via simulation-based inference. These analyses will produce cosmological information of reference quality, provided by the following deliverables: joint constraints from galaxy clustering and weak gravitational lensing on the acceleration of the expansion, and on neutrino masses.
The fellowship will be hosted at the Imperial Centre for Inference and Cosmology.