Map-based simulation-based inference for cosmology from galaxy clusters and weak lensing

lensing (WL), each of which adds systematic uncertainty, much of it tied to poorly understood baryonic physics.

This project will develop a more direct route. Starting from maps of the tSZ and the weak lensing signal, the student will train a Neural Network to extract optimal summary statistics from these data using methods such as variational mutual information maximisation. We will employ the the CosmoGridV1 suite of N-body simulations to train this network. With this, the student will then perform simulation-based inference to constrain cosmological parameters directly from these data using neural likelihood estimation, thus bypassing cluster finding and mass calibration altogether. This will build on the baryonification model that we have developed to paint gas onto the CosmoGridV1 N-body simulation suite and our current efforts to develop SBI methods for cluster cosmology and mass calibration using traditional cluster finding approaches. To ensure the inference is robust to how gas is modelled, the approach will be tested on independent simulation suites (e.g. AbacusSummit, hydrodynamical simulations), which employ complementary methods (analytical, Machine Learning-based) to generate the Compton-y signal.

The work will build up in complexity. A first proof of concept will assume the 3D halo centres are known exactly and quantify how well cosmology can be recovered. Realism will then be added step by step: redshift scatter, centres taken from the Compton-y maps, variation of the baryon-painting parameters (including the non-thermal pressure fraction), testing on a different simulation, scale cuts or low-pass filtering of the lensing maps (e.g. ℓ < 3000) to suppress small-scale baryonic effects, and using the tSZ map itself without peak finding. This work will result in an SBI-based joint analysis of tSZ observations from the Simons Observatory (SO) and weak lensing from the Rubin Observatory Legacy Survey of Space and Time (LSST) using ML-derived summary statistics and will deliver robust and tight constraints on the parameters underlying our cosmological model LCDM.

The student will be jointly supervised by Andrina Nicola (University of Manchester) and Boryana Hadzhiyska (Institute of Astronomy, University of Cambridge) in collaboration with Jake Snell (University of Cambridge), and will gain expertise in weak lensing, the Sunyaev-Zel'dovich effect, cosmological simulations and machine-learning-based inference, with direct application to current and upcoming lensing and CMB surveys, with multiple planned visits between the two institutions.

Desirable Student Background:

This project will require interest in cosmology and Machine Learning, good coding skills (especially python), as well as experience with Machine Learning and cosmological data analysis and inference.

How to apply:

Please apply under the University of Manchester application portal and select 'PhD in Artificial Intelligence' (OAA Application form).

When applying, you’ll need to specify the full name of this project, the name of your supervisor, if you already having funding or if you wish to be considered for available funding through the university, details of your previous study, and names and contact details of two referees.

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

You will be asked to upload the following supporting documents:

• Final Transcript and certificates of all awarded university level qualifications
• Interim Transcript of any university level qualifications in progress
• CV
• Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it).
• Contact details for two referees (Please ensure referee email addresses are official university or workplace accounts, as we may need to verify the references.)
• English Language Certificate (if applicable)

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs. Our selection process is free from bias, and we are committed to ensuring fair and equal opportunities for all applicants.

We are dedicated to supporting work-life balance and offer flexible working arrangements to accommodate individual needs, including a part-time study option.

Join us and help grow the UK’s national capability in artificial intelligence, part of the UK’s modern industrial strategy. Everyone is welcome if you are passionate about uncertainty quantification, decision making with humans in the loop and decision making for machine learning systems and their applications in Science and Engineering, and looking to develop your research and innovation skills at the doctoral level to further your career in the AI sector.

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact. (Equality, diversity and inclusion | The University of Manchester)

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

Funding Notes

Fully funded AI UKRI CDT 4 year program; The Project will be based in University of Manchester.

The UKRI AI CDT in Decision Making for Complex Systems is part of the TechFirst offering stipends of £30,000 for students eligible for home fee status. The start date is September 2027.

References

• CosmoGridV1 simulation suite: https://ui.adsabs.harvard.edu/abs/2023JCAP...02..050K/abstract
• Baryonification: https://ui.adsabs.harvard.edu/abs/2025JCAP...12..043S/abstract
• BIND baryon pasting method: https://ui.adsabs.harvard.edu/abs/2026arXiv260910709L/abstract
• Envisioned SBI methodology: https://ui.adsabs.harvard.edu/abs/2026PhRvD.113h3501T/abstract