Current and upcoming cosmological surveys, notably the Euclid space mission and neutral hydrogen (HI) 21-cm intensity mapping experiments offer a unique opportunity to improve our understanding of the formation of large-scale structures and the physical content of the Universe. These complementary probes trace the complex large-scale structure of the cosmos in different ways: weak gravitational lensing, as measured by Euclid, probes the gravitational effects of matter integrated along the line of sight, while HI intensity mapping traces the distribution of matter through the redshifted 21-cm emission of neutral hydrogen  in 3D. Their joint analysis could reveal cosmological information that is difficult to access using either probe independently. However, exploiting this complementarity remains challenging because of the computational cost of physical simulations, the complexity of astrophysical and instrumental effects, and the difficulty of extracting the full information encoded in non-Gaussian cosmic fields.
This PhD project will develop novel artificial intelligence methods for forward modelling the joint cosmological signal and perform parameter inference from these multi-tracer observations. First, neural generative models will be developed to learn the mapping between cosmological parameters, the underlying matter distribution, and synthetic weak-lensing and HI intensity maps. These models will aim to accelerate the generation of realistic simulations while preserving the statistical properties of each probe and the correlations between them. Second, neural representations will be investigated to jointly analyse Euclid weak-lensing maps and HI intensity maps, with the goal of exploiting non-Gaussian information and complementary cross-tracer correlations. Finally, these developments will be integrated into simulation-based inference frameworks to constrain cosmological parameters while accounting for astrophysical and instrumental uncertainties.
The methodology will be validated using realistic simulations and applied to available MeerKAT HI intensity-mapping data in combination with Euclid weak-lensing observations, as thanks to the relevant data becoming available already in the coming years. This will provide a concrete observational testbed from the outset of the project. The methods will also be designed to support future applications to SKA-Mid and the next generation of radio surveys. The project aims to establish an AI-driven framework for cosmological inference that more fully exploits the joint information provided by multiple tracers, opening new perspectives for studying dark matter and the formation of cosmic structures.

Related papers:
- A. Gkogkou, V. Bonjean, J.-L. Starck, M.  Spinelli  and P. Tsakalides, 'Foreground removal in HI 21 cm intensity mapping under frequency-dependent beam distortions',  Astronomy and Astrophysics,  706, id.A3, 2026.
- A. Gkogkou et al, 'JESSNet: Joint dEconvolution and Sparse Separation Network for HI 21 cm foreground cleaning', in press, 2026.
- P. Gorbatchev et al, 'Impact of HI intensity maps foreground cleaning methods on cosmological parameter recovery', submitted, 2026.
- P. Gorbatchev, J.-L. Starck, S. Camera and M. Spinelli, 'Beyond power spectrum to unveil systematics on Hi intensity maps', Astronomy and Astrophysics, 710, id.A154, 2026.
- H. Leterme, A. Tersenov, J. Fadili and J.-L. Starck,'A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping', Astronomy and Astrophysics,  710, id.A292, 2026.
- S. Majumdar et al, 'Cosmology with Intensity Mapping via statistics beyond the power spectrum in the SKAO Era', Advancing Astrophysics with the SKA II (AASKAII), 2026.
- A. Tersenov, S. Guerrini, J.-L. Starck and M. Kilbinger,  'The joint wavelet l1-norm matches neural compression for tomographic weak-lensing inference', submitted, 2026.
- V. Tinnaneri Sreekanth, S. Codis, A. Barthelemy, and J.-L. Starck, 'Theoretical wavelet l1-norm from one-point PDF prediction', Astronomy and Astrophysics,  691, id.A80, 2024.
- V. Tinnaneri Sreekanth, J.-L. Starck and S. Codis, 'Generative modeling of convergence maps based in LDT theoretical prediction', Astronomy and Astrophysics,  701, id.A170, 2025.