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

Session

Cosmology

3 Sept 2026, 09:30

Aula Prima (University of Malta)

40. [INVITED] ML methods in Cosmology

Angus H. Wright

03/09/2026, 09:30

41. CosmoGen: A genetic algorithm framework for the exploration of dark energy dynamics

Diogo Castelão

03/09/2026, 10:10

The standard Lambda cold dark matter (LCDM) paradigm of the physical Universe suffers from well-known conceptual problems and is challenged by observational data. Alternative models exist in the literature, both phenomenological and physically motivated, but many of them suffer from similar or new problems.

We propose a method to mechanically generate alternative models in a data-informed...

42. Emulating the Impact of Dark Matter Physics and Baryonic Effects on Milky Way–like Galaxies with the DREAMS Simulations

Li-Wen Liao

03/09/2026, 10:30

Although the LambdaCDM model can explain large-scale cosmological structures very well, some small-scale observations still do not match CDM predictions. To explain the discrepancy between theory and observations, warm dark matter (WDM) has been proposed as a possible explanation. Motivated by this idea, the DREAMS simulation suite is designed to explore the impact of WDM on galaxy formation....

43. Simulation-based tension quantification of the cosmic dipole anomaly

Mali Land-Strykowski (Sydney Institute for Astronomy, The University of Sydney)

03/09/2026, 11:10

The cosmic dipole observed in the matter distribution of galaxy surveys consistently disagrees with the kinematic expectation set by the cosmic microwave background, posing a serious challenge to the Cosmological Principle and the standard model of cosmology. However, the fidelity of the dipoles we infer rests on our understanding of the systematics present in the surveys. For many...

44. Field-level Bayesian Reconstruction of Cosmological Initial Conditions during the Epoch of Reionization

Nikolaos Triantafyllou (Scuola Normale Superiore)

03/09/2026, 11:30

Moments after the Big Bang, quantum fluctuations in the early Universe were scaled up to cosmological scales by inflation, shaping the initial distribution of matter (Initial Conditions, ICs) into a clumpy (nearly) Gaussian random field at the time of recombination. The first stars and galaxies formed in the densest of these clumps and shone light into the Universe. Their radiation ionized...

45. Wising up to CatWISE: using simulation-based inference to measure the cosmic dipole

Oliver Oayda

03/09/2026, 11:50

We apply Simulation-Based Inference ('SBI') to the cosmic dipole problem for the first time, measuring the distribution of quasar counts over the sky in the infrared CatWISE2020 ('CatWISE') sample. Our SBI-based approach alleviates systematic effects arising from the WISE instrument itself and enables direct inference of the sample's dipole. We find a dipole that is twice as large as the CMB...

50. Deep learning galaxy morphologies for shear calibration in the era of Euclid

Benjamin Csizi

03/09/2026, 14:00

For weak lensing cosmological surveys, accurate measurement of galaxy shapes is of paramount importance. This requires image simulations that match the real data as closely as possible. So far, these simulations rely on parametric surface brightness profiles, but do not account for complex morphologies and substructure. In the era of Euclid, this can however lead to biases, given the...

51. Symbolically regressing dark matter halo profiles using weak lensing

Alicia Martin

03/09/2026, 14:20

The structure of dark matter haloes is often described by radial density profiles motivated by cosmological simulations. These are
typically assumed to have a fixed functional form (e.g. NFW), with some free parameters. However, relying on simulations has
the disadvantage that the resulting profiles depend on the dark matter model and the baryonic physics implementation, which are
highly...

52. Machine Learning Methods for Stellar Collisions

Elena Gonzalez Prieto

03/09/2026, 14:40

Stellar collisions can occur frequently in dense cluster environments, and play a crucial role in producing exotic phenomena from blue stragglers in globular clusters to high-energy transients in galactic nuclei. Successive collisions and mergers of massive stars could also lead to the formation of massive black holes, serving as seeds for supermassive black hole in the early universe. While...

53. Anomalous Dark Matter Halos with Normalizing Flows

Boris Khanikati

03/09/2026, 15:00

Machine learning has become a powerful tool for characterizing the high-dimensional structure of cosmological simulations, yet identifying rare or physically distinct objects in a statistically principled way remains challenging. In this work, we develop a normalizing flow based framework to detect low-probability dark matter halos based on their intrinsic properties. Using simulations from...

54. Generalizing ICL Predictions Across Simulations: A Velocity Dispersion-Based Deep Learning Approach

Pablo Agustin Martin Torres, Marta Barroso

03/09/2026, 15:20

Context. Intracluster light (ICL) is a key tracer of the dynamical history of galaxy clusters, yet its extraction from simulations remains challenging. Velocity dispersion, which reflects the effects of mergers and interactions, provides an alternative approach for estimating ICL distribution.
Aims. We aim to develop a generalizable machine learning framework to infer ICL properties from...

55. Linking Radiation Transfer in the ISM to Cosmological Evolution using Symbolic Regression

Diane Salim

03/09/2026, 15:40

The intensity of the far-ultraviolet (FUV) interstellar radiation field (G0) in galaxies plays a critical role in dictating the thermal and chemical structure of the interstellar medium (ISM), which in turn is fundamental to regulating the star formation rate (SFR) and the subsequent picture of galaxy evolution that the SFR paints. However, efforts to develop closed-form analytic expressions...

58. How do generative diffusion models learn cosmic web environments?

Mehdi Noor

04/09/2026, 09:00

In recent years, generative diffusion models have made rapid progress in learning complex data distributions. In cosmology, they have shown promising results in emulating simulations across diverse cosmological settings, mitigating their computational cost while capturing not only two-point statistics but also higher-order correlations. In this work, we construct an optimised generative...

59. Digital Twins of the Universe: Machine Learning Against Cosmological Bias

Jenny Sorce

04/09/2026, 09:20

Modern cosmology faces a data problem. Progress is no longer limited by the volume of observations, but by the ability to process, interpret, and control biases in massive datasets. As surveys push measurements of large-scale structure to percent-level precision, tensions with the standard cosmological model have emerged, many of which may reflect systematic effects rather than new physics....

60. AI-Assisted Super-Resolution-Simulations: application to the DEMNUni-Cov N-Body simulations

Bruno Montalto

04/09/2026, 09:40

Cosmological simulations are a fundamental tool for studying the evolution of the Universe and the formation of structures across different scales. However, their computational cost increases steeply with resolution, thus limiting the scales and level of detail that can be achieved. Consequently, a trade-off between simulated volume and spatial resolution is typically required.
This...