The goal of this PhD project is to develop all necessary tools for an efficient and reliable analysis of Euclid weak-lensing and lensing - galaxy cross-correlation data. Starting from the measured Euclid weak-lensing galaxy shapes and spectroscopic galaxy data from Euclid and other surveys such as BOSS, eBOSS, and DESI, the student will construct various estimators of lensing and cross-correlation observables.

Combinations of these observables as function of scale, redshift, and galaxy properties will be optimised to maximally extract cosmological information from the data. In addition, detailed modelling of systematic effects will be carried out to control and minimize their influence on the results.



The student will make use of, and further develop modern statistical inference tools for efficient parameter inference. Theoretical predictions of observables from models of the expansion history and large-scale structure of the Universe will be created in an automatic differentiation framework, e.g. using the library jaxcosmo, exploiting massive parallel computations on GPUs and the ability to compute

gradients of the likelihood to accelerate inference. This will open up efficient (Bayesian) inference methods that make use of the gradients of the models with respect to parameters. Compared to traditional sampling techniques, these methods offer a significant computation time speed-up, and the ability to efficiently explore a large number of parameters. This is important for exploring non-standard models of gravity with additional parameters, and flexible models with many nuisance parameters. It also allows us to include detailed, time-consuming modelling of systematic and higher-order effects.

Weak gravitational lensing, the distortion of images of high-redshift galaxies due to foreground matter structures on large scales, is one of the most promising tools of cosmology to probe the dark sector of the Universe.



The student will work on various wide-field instruments which provide imaging surveys of galaxies:

- UNIONS, the Ultraviolet Near-Infrared Optical Northern Sky survey, an ongoing large imaging survey of the Northern sky in 5 bands.

- WFST, the Wide-Field Survey Telescope. This 2.5m optical telescope under construction in China will observe the Northern sky in 5 bands.

- CSST, the 2m Chinese Space Station Telescope, to be launched in 2024 into a low Earth orbit, will observe an area of 17,500 deg2 in 7 optical and near-UV bands.



Starting with ShapePipe, a weak-lensing pipeline created by our group, the student will develop new methods to analyse such large

datasets. The unprecedented statistical precision will require novel approaches for quantifying observational and instrumental systematic errors. Astrophysical and cosmological observables will be modeled carefully for robust and reliable inference of dark-matter and dark-energy properties from weak-lensing data.

Cosmology in the 21st century aims to better our understanding of the Universe by seeking to answer open questions concerning the nature of dark matter and dark energy, and the precise expansion rate of the Universe. In order to tackle these questions, it is essential to take advantage of all the data made available in the current era of multi-messenger astronomy, capitalising on the latest advances in signal processing, machine learning and the handling of big data.

Current and upcoming optical surveys, such as KiDS-450 [1], the Dark Energy Survey Year 1 [2], the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) [3] and Euclid [4], are probing wider and deeper patches of the late-time Universe to improve constraints on cosmological parameters by measuring the shapes and distributions of galaxies. These parameters can be compared to the latest analyses of the cosmic microwave background radiation (i.e. the early Universe) by surveys like Planck [5]. Recent studies [6] highlight some discrepancy between early and late-time analyses indicating some systematic uncertainty or an incomplete model of cosmology.

Additionally, In recent years gravitational wave interferometers, such as LIGO and Virgo, have pushed astronomical observations beyond the electromagnetic spectrum. This has made it possible to detect the interaction of distant neutron stars and black holes.

Radio wavelengths provide a complementary and independent probe of the late-time Universe. Radio astronomy provides the advantage of probing higher redshifts, having a deterministic point spread function (PSF) and being less sensitive to PSF anisotropies [7]. Cross-correlations between radio and optical surveys can additionally alleviate systematics effects such as intrinsic alignments improving cosmological constraints [8-9]. Upcoming radio surveys, such as the Square Kilometre Array (SKA), are designed to reach an order of magnitude greater sensitivity and survey speed than existing instruments. SKA has the potential to add significant additional constraints on cosmological parameters given the vast sky area it will cover (~75%). This, however, comes at the cost of having to manage extremely large scales of data and complicated image reconstruction. SKA is expected to produce ~1 TB of data every second. With typical observations taking ~6h and a total lifespan of 15 years, SKA will produce data in the Exabyte (1018 bytes) scale [10], making it one of the biggest data management problems in modern science.

The CosmoStat team has been pioneering the use of signal processing and machine learning techniques for solving inverse problems in astronomical image reconstruction. Applying these methods to radio-interferometric data, however, brings a host of new challenges, in part due to the additional complexity of the inverse problems to solve, but also due to the extremely large-scale dimensions of the problem. Conventional deep learning approaches will not be able to scale to the typical size of an SKA field, and developing efficient model-parallelism approaches will be necessary.

While the Universe is expanding with increasing velocity, the question of what is causing cosmic acceleration remains unsolved. Acceleration seems to act against gravitational attraction, as if a new source of energy, dubbed dark energy, were responsible for it. In addition, neutrino masses are yet to be measured and are degenerate with dark energy evolution.



This PhD proposal is meant to contribute to the Euclid mission, by combining information from galaxy clustering and weak lensing, and integrate it in the Euclid Consortium validated likelihood.

The PhD student will be able to work at the interface between data and theory and concretely collaborate with a large collaboration like the Euclid satellite. Objectives include 1) extending the likelihood software to include early dark energy models 2) contribute to the collaboration effort on comparing theoretical predictions with data and the cross-correlation between galaxy clustering and weak lensing 3) investigate different survey samples and statistics to break the degeneracy between neutrino masses and dark energy evolution.