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

Session

Observational Surveys

31 Aug 2026, 09:50

Aula Prima (University of Malta)

1. [INVITED] Deep Learning the Physics of Galaxy Formation Using Large Observational Surveys

Marc Huertas-Company (IAC)

31/08/2026, 09:50

2. What Comparing HST and JWST Images Reveals About Stellar Clusters Without Labels

Nils Guillaume Francois Dani Candebat (Istituto Nazionale di Astrofisica (INAF))

31/08/2026, 10:40

Step outside your office with a printed sheet of SDSS galaxy images and ask a random passerby to sort them into categories. Even without scientific training, most people can readily distinguish early- from late-
type galaxies, the same intuition that motivated Galaxy Zoo’s landmark citizen science initiative [Lintott et al., 2011]. This simple observation underscores a profound principle: the...

3. J-Patch: A Transformer architecture for missing data imputation and anomaly detection in photometric SEDs

Antonio Hernán Caballero (Centro de Estudios de Física del Cosmos de Aragón (CEFCA))

31/08/2026, 11:00

Large-area multi-band photometric surveys produce expansive catalogs of spectral energy distributions (SEDs) that frequently contain missing or compromised data points due to image artifacts. Standard machine learning architectures are brittle to incomplete sequences, while traditional template-fitting imputation scales poorly and systematically underestimates uncertainties. We present...

4. Rethinking Gaia XP Spectra for Machine Learning: Truncation, Basis Design, and Functional Methods

Aidas Medžiūnas

31/08/2026, 11:40

The low-resolution BP/RP (XP) spectra from Gaia Data Release 3 provide an unprecedented spectrophotometric dataset for hundreds of millions of sources, encoded as coefficients in a Hermite-function basis rather than as sampled fluxes. This representation poses both challenges and opportunities for machine learning (ML) applications. In this work, we investigate Gaia XP spectra as an object of...

5. Multimodal contrastive learning for white dwarf classification with Gaia

Xabier Pérez Couto

31/08/2026, 12:00

We present a self-supervised contrastive learning approach for the classification of white dwarf atmospheric types using Gaia DR3 data. Our framework combines XP spectral coefficients with photometric and astrometric features through a multi-branch neural encoder, trained without spectroscopic labels using physically motivated augmentations. The resulting embeddings show substantially improved...

6. Koexai’s AI Pipelines for Astrophysical Data Analysis: from Supernovae to the Cosmic Web

Luca Naso (Koexai s.r.l.)

31/08/2026, 12:20

Koexai is a deep-tech company working at the interface between machine learning, scientific software, and astrophysical data analysis. Over the last year, within the INAF / PNRR / ICSC Spoke 3 context, Koexai has supported several research groups on projects spanning supernova characterisation, cosmological simulations, pulsar-timing inference, X-ray background mitigation, and gamma-ray...

7. [INVITED] Astronomical Image Denoising

Pierpaolo Brutti

31/08/2026, 14:00

8. STRADAViT: Self-Supervised Transfer for Radio Astronomy Vision Transformers

Andrea DeMarco (University of Malta)

31/08/2026, 14:40

Next-generation radio astronomy surveys are delivering millions of resolved sources, but robust and scalable morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present STRADAViT, a self-supervised Vision Transformer (ViT) continued-pretraining framework for learning transferable encoders from radio astronomy imagery. The framework combines...

9. Quantifying Data Curation Effects in Vision Transformers for Radio Astronomy Imaging

Hayley Camilleri

31/08/2026, 15:00

Vision Transformers (ViTs) are increasingly being adopted for large-scale astronomical imaging, yet discussion of performance often centers on model scale and dataset size rather than on the quality and structure of the training data themselves. In radio astronomy, this is a significant omission: images are frequently sparse, background-dominated, and affected by instrumental artefacts, tiling...

10. Self-Supervised Neural Networks for High-Resolution Radio Imaging

Shunyuan Mao

31/08/2026, 15:20

Image reconstruction in radio interferometry is a classic ill-posed inverse problem: recovering a continuous sky brightness distribution from sparse Fourier (uv-plane) samples. While the standard CLEAN algorithm is robust for point sources, it often introduces artifacts when imaging extended, diffuse structures. Regularized Maximum Likelihood (RML) methods offer an alternative but face...

11. Multi-Wavelength AGN Identification and Physical Parameter Estimation using machine learning in DESI survey.

Dhavala Sai Srinivas

31/08/2026, 16:10

Active Galactic Nuclei (AGN) play a key role in galaxy evolution, but identifying a complete, unbiased sample is complicated because it requires extensive, multi-wavelength detections. To address this, we apply a machine learning framework to the Dark Energy Spectroscopic Instrument (DESI) survey, utilizing the available rest-frame UV and optical range (3600–9800 Å) to leverage established,...