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

Session

Flash Talks

31 Aug 2026, 16:30

Aula Prima (University of Malta)

12. Machine Learning and Super resolution for radio astronomical data analysis

Giorgia Vitanza

31/08/2026, 16:30

The main objective of the project is to improve the quality and usability of data from next-generation radio telescopes, such as those involved in the SKA project, by developing advanced methodologies to increase resolution, automatically remove artifacts, and intelligently compress datasets to optimize storage. These datasets, extremely large and multidimensional, require innovative solutions...

13. Classification Accuracy as a Spectral Similarity Metric for Planetary Bodies

Ramapriya Ramamoorthy

31/08/2026, 16:35

Hyperspectral data received from missions like Cassini-Huygens provide detailed insights about the surface composition of bodies in the outer solar system. However, existing spectral classification methods either require heavy computation, extensive observational data, or manual fine-tuning of features. This study proposes the use of classification accuracy as a similarity metric, using a...

14. Inter-annotator consensus: Towards Automated Detection of Low Surface Brightness Features in Large-Scale Surveys

Renaud Vancoellie

31/08/2026, 16:40

With the arrival of Euclid/LSST and other large-scale surveys we address the automatic detection and segmentation of galactic features from deep sky images. The training of machine learning and deep learning systems requires manual annotations, which tend to present a high variability between annotators. For complex astrophysical features such as low surface brightness collision debris, even...

15. A Benchmark of Convolutional Neural Networks and Vision Transformers for Galaxy Morphology Classification

Liam Azzopardi

31/08/2026, 16:45

Modern sky surveys generate images of galaxies at a scale that renders manual classification as an unscalable task, motivating the development of automated deep learning pipelines. This study presents a systematic comparison of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for automated galaxy morphology classification on a curated subset of the Galaxy Zoo 2 dataset. To...

16. Reconstructing the Milky Way's Merger History with Graph Attention Networks

Milan Quandt Rodriguez

31/08/2026, 16:50

Unraveling the building blocks of the Milky Way’s stellar halo is typically achieved using either stellar dynamics or chemical abundances. While abundances are generally considered more robust tracers, clustering in this high-dimensional chemical space is highly non-trivial: measurement noise at low [Fe/H], the intrinsic scatter of distinct systems, and, most importantly, the heavy overlap of...

17. Extracting 1D Spectra from 2D Spectrograms using Deep Learning

Soorya Narayan Rajeshkumar

31/08/2026, 16:55

With telescopes like the Euclid in orbit and CSST in the works, we are looking at an emerging era of astronomy with surveys producing millions and millions of slitless spectrograms. While the analytical extraction techniques are sufficient for the current amount of data, the community will require faster pipelines for future data releases. Added to the large amount of expected data, slit-less...

24. MAGI (Multivariate Autoencoder for particle Generative Inference): a generative ML framework for accelerated multi-stage particle simulations

Francesco Monastra (Istituto Nazionale di Astrofisica (INAF))

01/09/2026, 11:50

Detailed particle transport simulations, particularly through the Geant4 toolkit, are essential for evaluating instrumental particle backgrounds that ultimately limit detector sensitivity and drive the design and optimization of low-background instrumentation in high-energy astrophysics and particle physics. By propagating incident particle fluxes through detailed mass models and simulating...

25. Machine Learning for All-Sky Image Classification and Cloud Detection

Ricardo Zanmar Sanchez (Istituto Nazionale di Astrofisica (INAF))

01/09/2026, 11:55

Continuous monitoring of environmental conditions at astronomical observatories is critical for optimizing operations and assessing the integrity of observational data. All-sky cameras are a cost-effective way to capture a 180° field of view to monitor clouds and other adverse weather. In this work, we apply several supervised ML algorithms to classify all-sky images by atmospheric condition....

67. Leveraging Transformer Architectures for Scalable Exoplanet Transit Inference

Vikash Singh

01/09/2026, 15:30

74. Morphological Classification of VLBI Images of Jets in Active Galactic Nuclei Using Contrastive Learning

Dmitrii Zagorulia

02/09/2026, 12:10

75. Characterization of the environment of the CHANCES Low-z subsurvey using Machine Learning Techniques [TBC]

Franco Piraino-Cerda

02/09/2026, 12:15

81. Tracing galaxy evolution with autoencoders

Leon Butterworth

02/09/2026, 15:20

82. Spectroscopy fine-tuning: the last step of machine-learning approach for UDGs

Antonio Vanzanella

02/09/2026, 15:25

46. Robust Dark Matter Inference from Galaxy Clusters via Domain-Adapted Machine Learning

Ethan Tregidga

03/09/2026, 12:10

Dark matter accounts for 85% of all matter in the Universe, yet its nature remains elusive. Next generation telescopes are providing us with a wealth of observations of dark matter dominated galaxy clusters that have embedded within them subtle clues to its nature. However, traditional methods either compress the data into summary statistics or require computationally expensive forward...

47. Score-Based Diffusion for Low-ℓ Primordial CMB B-Mode Reconstruction

Anumanchi Agastya Sai Ram Likhit

03/09/2026, 12:15

Detecting primordial B-mode polarization of the Cosmic Microwave Background (CMB) provides a direct probe of inflationary gravitational waves. However, the signal is extremely faint and contaminated by gravitational lensing, instrumental noise, and astrophysical foregrounds. Here we present a score-based diffusion approach, formulated using variance-exploding stochastic differential equations...

48. Robust Cosmological Parameter Inference from Galaxy Properties using Conditional Normalizing Flows

Tirthankar De

03/09/2026, 12:20

The internal properties of individual galaxies carry information about the cosmological environment in which they formed. Extracting this information robustly, while marginalizing over uncertain astrophysical processes, remains an open challenge. We present an amortized simulation-based inference framework using conditional normalizing flows to estimate joint posteriors over cosmological and...

49. Hub-Aware Hybrid Search: Accelerating the Locally Aligned Ant Technique

Simone Vilardi

03/09/2026, 12:25

Finding manifold structures in noisy and high-dimensional point clouds is a challenging but important problem. In astronomical observation survey and simulation data the detection of filaments, streams (1D), walls (2D) and clusters (3D) gives rise to deeper understanding of the evolution of our universe. The Locally Aligned Ant Technique (LAAT) uses biologically inspired agents to efficiently...

56. ML algorithm for automatic determination of the dynamical state of GCs based on CMC-SURVEY — a public, open-access, fully downloadable database of detailed globular cluster models

Arkadiusz Hypki (Faculty of Mathematics and Computer Science of Adam Mickiewicz University, Nicolaus Copernicus Astronomical Center of the Polish Academy of Sciences)

03/09/2026, 16:00

The CMC code is a well-recognized numerical tool capable of performing
detailed simulations of realistic, large globular clusters within just a
few days. It is a fully featured code that produces results comparable
to state-of-the-art direct N-body simulations.

The CMC code has been used to generate an extensive database of detailed
numerical models for a wide range of initial...

57. Hierarchical Simulation Based Inference for Milky Way potential reconstruction

Giuseppe Viterbo

03/09/2026, 16:05

Stellar streams — the tidally disrupted remnants of globular clusters and dwarf galaxies — serve as sensitive dynamical tracers of the Milky Way's gravitational potential. Inferring the potential's parameters from an ensemble of observed streams is naturally cast as a large-scale hierarchical Bayesian problem: each stream constrains local orbital properties while all streams jointly inform...

61. Deep Learning surrogate models to accelerate on the fly Radiative Transfer in simulations

Lorenzo Branca

04/09/2026, 10:00

Radiative transfer is a fundamental ingredient of computational astrophysics, essential both for interpreting observations and for modeling the thermal and dynamical impact of radiation on astrophysical systems. Yet accurate radiative transfer remains one of the main computational bottlenecks in modern simulations. Standard approaches such as ray tracing and Monte Carlo methods are powerful...

35. A Generative Machine Learning Approach for Molecular Characterization of Astronomical Spectra

Gabriel Jaimes-Illanes

04/09/2026, 15:00

Nowadays, the use of high-sensitivity astronomical facilities such as the Atacama Large Millimeter/submillimeter Array (ALMA) has opened important applications of data science for the detection of new species in the interstellar medium. However, tools for analyzing and interpreting these complex datasets have not yet reached their full potential. The increasing availability of observational...

36. Aligning Chandra X-ray spectra and scientific literature

Nicolo' Oreste Pinciroli Vago

04/09/2026, 15:05

Upcoming astronomical surveys are projected to produce petabyte-scale datasets, necessitating the development of intelligent, multimodal foundation models to accelerate scientific insight. While traditional data analysis often treats observational products and scientific literature as isolated domains, this work presents a novel contrastive learning pipeline that aligns Chandra X-ray spectra...

37. Maria-Nifty: Gaussian Process-Based Imaging of (Sub-)Millimeter Single-Dish Telescope Data

Richard Fuchs

04/09/2026, 15:10

(Sub-)millimeter single-dish telescopes observe larger spatial scales and feature faster mapping speeds than radio interferometers. However, their measured signals are dominated by atmospheric fluctuations and instrumental noise, making it difficult to recover the true astronomical sky. We introduce maria-nifty, a Gaussian process-based framework for reconstructing sky maps from single-dish...

38. Analysis of GAN as a Tool for Data Augmentation

Yagyasha Rastogi

04/09/2026, 15:15

This work investigates the use of Generative Adversarial Networks (GANs) as a tool for data augmentation in astronomical galaxy classification. Convolutional Neural Networks (CNNs) generally perform better with large datasets, but astronomical datasets often suffer from class imbalance and limited samples for rare galaxy types. To address this, a GAN model was trained to generate synthetic...

39. Normalizing Every Stellar Spectrum

Tomasz Rozanski

04/09/2026, 15:20

Continuum normalization is a crucial but often underappreciated step in stellar spectroscopy. Errors in continuum placement bias comparisons between observed and synthetic spectra, and can propagate into stellar parameters, chemical abundances, and radial velocities. They are especially problematic for wide spectral features, where coherent normalization residuals can mimic or obscure...

34. Probabilistic Spectral Super-Resolution: Bridging Gaia DR3 XP and SDSS for Object Sub-Classification

Andrija Zupic

04/09/2026, 15:25

Recent studies show that Gaia Data Release 3 (DR3) low-resolution XP spectra can reliably recover bulk stellar properties like global metallicity. However, their coarse resolution limits our ability to classify objects based on specific spectral features. Tasks such as separating DA white dwarfs from A-type stars without reliable parallaxes or distinguishing broad from narrow emission lines to...

26. TO BE CONFIRMED