International Conference on Machine Learning for Astrophysics 3rd Ed. - ML4ASTRO3

Session

Generative AI

4 Sept 2026, 10:05

Aula Prima (University of Malta)

27. [INVITED] AI agents for accelerating astronomical research and discoveries

Francisco Villaescusa-Navarro (Flatiron Institute / Princeton University)

04/09/2026, 10:05

28. [INVITED] How astronomers use LLMs? Emerging results and ethical concerns from professional surveys

Morgan Fouesneau

04/09/2026, 11:10

29. A Multi-Resolution, Data-Driven Transformer Framework for Radio Interferometric Imaging

Qitong Anabel Tan

04/09/2026, 11:50

Radio interferometric imaging reconstructs sky brightness distributions from sparse Fourier measurements, forming a highly ill-posed inverse problem that is increasingly challenged by the scale and resolution demands of next-generation telescopes, such as the Square Kilometre Array (SKA). Classical approaches, including CLEAN and its variants, often struggle with extended emission and face...

30. Multi-Agent AI Systems for Astrophysics and Cosmology

Andrius Tamosiunas

04/09/2026, 12:10

Recent advances in large language and reasoning models have established such systems as valuable tools for assisting the scientific process. A key development has been the combination of these models into multi-agent systems that collaborate to perform a range of tasks, including writing, debugging, and executing scientific code, as well as conducting literature reviews and data analysis....

31. Deep Generative Priors for Interferometry: Non-Parametric Forward Modelling of the Sunyaev-Zeldovich Effect

Luca Fontana

04/09/2026, 14:00

Interferometric observations of the Sunyaev-Zeldovich (SZ) effect provide critical insights into galaxy clusters, yet extracting the diffuse, negative SZ signal from visibility data remains a profound inverse problem. Traditional imaging algorithms struggle with these signal properties, while standard forward modelling is inherently limited by strong parametric assumptions. In this talk, I...

32. Bayesian multiband imaging of SN1987A in the Large Magellanic Cloud with SRG/eROSITA

Vincent Eberle

04/09/2026, 14:20

The eROSITA Early Data Release (EDR) and eROSITA All-Sky Survey (eRASS1) data have already revealed a remarkable number of undiscovered X-ray sources. Using Bayesian inference and generative modeling techniques for X-ray imaging, we aim to increase the sensitivity and scientific value of these observations by denoising, deconvolving, and decomposing the X-ray sky. Leveraging information field...

33. Bayesian model selection for high-dimensional astrophysical generative models

Matteo Guardiani

04/09/2026, 14:40

Machine-learning approaches in astrophysics increasingly make use of high-dimensional generative models, for example in imaging, component separation, multi-instrument analysis, and simulation-based inference. These models enable flexible reconstructions and uncertainty quantification, but they also raise a central question: how can we decide whether additional model complexity is actually...