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

Contribution List

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

Malgorzata Siudek (Instituto de Astrofísica de Canarias)

31/08/2026, 09:50

Observational Surveys

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

Observational Surveys

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 (remote)

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

31/08/2026, 11:00

Observational Surveys

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

Observational Surveys

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

Observational Surveys

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. The Koexai Approach to Astro-ML Research: From Physical Models to Machine Learning

Luca Naso (Koexai s.r.l.)

31/08/2026, 12:20

Observational Surveys

Koexai is a deep-tech company working at the interface between machine learning and astrophysical research, supporting scientific teams in translating domain-specific problems into robust ML workflows. Over the last year, within the INAF / PNRR / ICSC Spoke 3 context, Koexai has worked on five research projects spanning supernova characterisation, cosmological simulations, pulsar-timing...

7. [INVITED] Astronomical Image Denoising

Pierpaolo Brutti

31/08/2026, 14:00

Observational Surveys

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

Andrea DeMarco (University of Malta)

31/08/2026, 14:40

Observational Surveys

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

Observational Surveys

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 (remote)

Shunyuan Mao

31/08/2026, 15:20

Observational Surveys

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

Observational Surveys

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,...

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

Giorgia Vitanza

31/08/2026, 16:30

Flash Talks

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 [remote]

Ramapriya Ramamoorthy

31/08/2026, 16:35

Flash Talks

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

Flash Talks

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

Flash Talks

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

Flash Talks

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

Flash Talks

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...

18. [INVITED] Machine Learning and Deep Learning in Astroparticle Physics: From Cosmic Rays to Neutrino and Gamma-Ray Astronomy

Yvonne Becherini

01/09/2026, 09:00

Astroparticles

19. From FermiLAT Unassociated Sources to Pulsar Discoveries: A Hierarchical Deep Learning 1DCNN Approach for Spectral Classification

Cristian Pozo González

01/09/2026, 09:40

Astroparticles

The Fermi Large Area Telescope (LAT) has significantly advanced our understanding of the high-energy gamma-ray sky, yet nearly one third of the sources in the Fourth Fermi-LAT Source Catalog (4FGL) remain unassociated with known astrophysical objects. Traditional machine learning approaches used to classify these sources typically treat spectral features as independent tabular variables and...

20. Prompt GRB classification through waterfalls and deep learning

Nicolò Cibrario

01/09/2026, 10:00

Astroparticles

Gamma-ray bursts (GRBs) are among the most energetic phenomena in the Universe and provide a unique laboratory for physics under extreme conditions. Rapid classification of GRBs based on their prompt emission is essential to guide timely multi-wavelength and multi-messenger follow-up observations.
In this work, we present a data-driven pipeline for GRB classification based on observations...

21. AI-based identification of high-redshift Gamma-ray Burst afterglows in Fermi-LAT observations

Riccardo Martinelli

01/09/2026, 10:20

Astroparticles

The detection of high-redshift (high-z) Gamma-ray Bursts (GRBs) can enhance our understanding of early universe phenomena, although detecting them in gamma-rays is challenging due to the sensitivity of current telescopes in this energy range.

We present a data-driven methodology based on an Artificial intelligence (AI) approach to identify faint high-z GRB signals in Fermi Large Area...

22. IROS Diffusion Imaging for the LEM-X Observatory

Edoardo Giancarli (Istituto Nazionale di Astrofisica (INAF))

01/09/2026, 11:10

Astroparticles

The LEM-X observatory is a proposed lunar coded-mask telescope for wide-field monitoring of steady and transient sources in the (2–50 keV) range, with a pathfinder under development with slated launch in the early 2030s [1]. Coded-mask imagers represent essential tools in high energy astrophysics, providing all-sky cover with respect to narrow FoV free-flier telescopes. However, these systems...

23. Classification of the Variability Patterns of the Black Hole Binary GRS 1915+105 Using Deep Learning

Kenji Yoshida

01/09/2026, 11:30

Astroparticles

The black hole binary GRS 1915+105, a typical microquasar, is known for its unique X-ray variability, which can reveal critical insights into black hole physics. Belloni et al. (2000) indicated that it is possible to classify its remarkable variability into twelve classes. This classification provides a foundation for exploring the physical phenomena underlying these variations, which can...

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

Flash Talks

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

Flash Talks

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....

62. [INVITED] AI for the Sky: SkAI Research and Physics-Informed Deep Learning for Stars (remote)

Vicky Kalogera

01/09/2026, 13:30

Time Domain And Transients

The coming decade of sky surveys will reveal the changing universe at a depth and scale we have never had. Meeting that opportunity takes more than better analysis — it reaches into how we choose what to observe, how we draw inference from what we collect, and how we model the physics underneath. I will introduce the NSF-Simons AI Institute for the Sky (SkAI), where astrophysicists, computer...

63. Speeding-up the search for anomalies in ZTF time series with interpretable machine learning

Emmanuel Gangler

01/09/2026, 14:10

Time Domain And Transients

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. Interpretable AI on the other hand provides clues to the analyst, often in the...

64. Using Flow Matching to study the sub-burst structure of short astronomical transients

Anja Schmit

01/09/2026, 14:30

Time Domain And Transients

Astronomical transients, including Fast Radio Bursts and Gamma-ray Bursts, are among the most energetic known phenomena in the universe, originating mostly from extragalactic distances. To gain deeper insight into their origin and nature, it is essential to analyse their substructure. A rapidly increasing number of observations allows for data-driven analysis of these transients. In this talk...

65. Bridging Difference Imaging and Supernova Physics with TimeGANs

Maria Zampella

01/09/2026, 14:50

Time Domain And Transients

Uncertainties in the mass-loss history model for stellar evolution hinder the development of a comprehensive understanding of the scenarios linking different supernova types to their progenitors.
This work aims to predict the physical parameters of the progenitor directly from time sequences of synthetic multi-band images. The focus is on redshift estimation.
We propose a generative method...

83. Leveraging Transformer Architectures for Scalable Exoplanet Transit Inference

Vikash Singh

01/09/2026, 15:10

Time Domain And Transients

Current exoplanet detection relies heavily on iterative sampling and box-searching, methods that face significant scaling challenges with the massive data volumes expected from next-generation surveys. This presentation introduces an exploratory concept: adapting Large Language Model (LLM) architectures to treat photometric light curves as "textual" sequences. By utilizing self-attention...

68. [INVITED] Machine Learning and Extragalactic Science

Nicola Rosario Napolitano

02/09/2026, 09:00

Extragalactic Science

69. More Than the Sum of Its Parts - Multimodal Learning for Galaxy Evolution in JWST Pure Parallel Observations

Ivelina Momcheva

02/09/2026, 09:40

Extragalactic Science

Deriving physical properties of galaxies requires high information density: deep multi-band imaging, spectroscopy, and/or photometric catalogs together. Deep fields provide this richness over small areas, but wide-field surveys cover larger volumes with far sparser data, creating a tension between area and information content.
We present a self-supervised multimodal framework that learns a...

70. Using UMAP Dimensionality Reduction to Map the Color-Redshift Relation

Finian Ashmead

02/09/2026, 10:00

Extragalactic Science

The volume of galaxy imaging data continues to outpace that of the high quality spectroscopic data used to precisely measure physical parameters like redshift and specific star formation rate (sSFR). In the near future, this problem will be exacerbated as facilities like Rubin and Roman rapidly build up sky maps extending to higher redshifts and dimmer fluxes, regimes where the spectroscopic...

71. Learning AGN multimodal characterization through optical, x-ray and host galaxy properties

Noemi Lery Borrelli

02/09/2026, 10:20

Extragalactic Science

Active Galactic Nuclei are powered by accretion onto supermassive black holes and exhibit a wide range of observed properties. While the Unified Model explains part of this diversity through orientation effects, growing observational evidence suggests that additional factors-such as evolutionary stage and host galaxy properties-also play a significant role.
In this context, we adopt a...

72. 4MOST/ByCycle: Detecting Cool Circumgalactic Medium Quasar Absorbers via Contextual Anomaly Detection with Variational Autoencoders

Nicolás Guerra-Varas

02/09/2026, 11:10

Extragalactic Science

Even though the gas around and between galaxies (the circumgalactic and intergalactic media, CGM and IGM) harbour about 90% of the baryons in the Universe, they remain significantly less understood than the matter associated with starlight from galaxies. In this talk, I will introduce the Baryon Cycle (ByCycle) project, a large high-resolution (R~20,000) 4MOST/VISTA spectroscopic survey that...

73. Needles in the cosmic haystack: Innovating the search for Reionization-Era quasars with self-supervised machine learning

Laura Natalia Martínez Ramírez

02/09/2026, 11:30

Extragalactic Science

Luminous quasars at the highest redshifts (z>6) are key laboratories for studying early supermassive black hole (SMBH) growth and the physical conditions of the Universe during cosmic reionization. However, their extremely low spatial density (< 1 per Gpc³), combined with severe contamination from foreground ultracool dwarfs that outnumber them by up to four orders of magnitude, makes their...

76. From Projection to Reconstruction: Predicting Peculiar Velocities and Internal Distances in Galaxy Clusters via Graph Neural Networks

Sheng-Chieh Lin

02/09/2026, 11:50

Extragalactic Science

Redshift measurements of galaxies within galaxy clusters play a crucial role in cosmology and astrophysics, serving as an essential key for cluster identification and related cosmological studies. However, the phase space distribution of cluster galaxies is not yet fully understood, as the observable positions and velocities are subject to line-of-sight projection effects. In this...

74. Simulation-to-Real Learning for VLBI Jet Morphology Classification

Dmitrii Zagorulia

02/09/2026, 12:10

Flash Talks

This work explores the application of machine learning techniques to classifying active galactic nuclei (AGN) with jets based on Very-Long-Baseline Interferometry (VLBI) observations at frequencies 1–90 GHz. Building upon previous work by Fanaroff and Riley, who classified relativistic jets in radio galaxies on kiloparsec scales, we extend this classification to parsec scales, closer to the...

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

Franco Piraino-Cerda

02/09/2026, 12:15

Flash Talks

Galaxy clusters, the most massive bound structures in the Universe, continue to grow by accreting galaxies from filaments, groups, and even other clusters. As a result, galaxy evolution is influenced by interactions with its dynamic environments. To quantify this environmental impact, we analyze the surroundings of 50 massive clusters (up to 5xR200) from the CHANCES low redshift subsurvey...

77. End-to-end differentiable forward modeling and inverse inference for integral field spectroscopic data

Anna Lena Schaible

02/09/2026, 14:00

Extragalactic Science

Modern integral field spectroscopic (IFS) observations provide unprecedented detail of galaxy dynamics, but directly comparing these integrated light observations to discrete particle outputs from cosmological simulations remains challenging. Traditional forward-modeling tools are often CPU-bound and lack derivative information, hindering their application in modern gradient-assisted machine...

78. J-PAS: A Neural Network Approach to Single Stellar Population Characterization (remote)

Helena Domínguez Sánchez

02/09/2026, 14:20

Extragalactic Science

J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg2 of the visible sky from Javalambre, capturing data in 56 narrow band filters. This survey promises to revolutionize galaxy evolution studies by observing ~10^8 galaxies with low spectral resolution. A crucial aspect of this analysis involves...

79. Deep learning approaches to galaxy merger identification and classification

Subhrata Dey

02/09/2026, 14:40

Extragalactic Science

Galaxy mergers are fundamental to the hierarchical assembly and evolution of galaxies, often driving starburst activity and AGN fueling. Identifying mergers and their stages, such as pre- and post-coalescence, from imaging alone, especially given the vast size of modern datasets, remains extremely challenging. We develop a supervised deep learning framework using Convolutional Neural Networks...

80. TORRCH: Field-level Reconstruction of Reionization Morphology from Galaxy Tracers (remote)

Soumak Maitra

02/09/2026, 15:00

Extragalactic Science

The spatial morphology of Reionization contains information that is largely compressed away by global measurements of the volume-averaged neutral fraction. Ionized bubbles, neutral islands, and their connection to the high-redshift galaxy distribution encode the topology of reionization and the nature of the ionizing sources. In this talk, I will present TORRCH (TOmographic Reconstruction of...

81. Tracing galaxy evolution with autoencoders

Leon Butterworth

02/09/2026, 15:20

Flash Talks

Galaxy morphology reveals insights into a galaxy’s properties and evolutionary history, however, galaxies (and their morphologies) evolve on timescales much longer than human lifetimes. Therefore studying the evolution of individual galaxies is difficult as we only see one brief stage of the galaxy’s evolution. We are fortunate that we are capable of observing many galaxies across cosmic time,...

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

Antonio Vanzanella

02/09/2026, 15:25

Flash Talks

Low Surface Brightness Galaxies(LSBGs) are defined as galaxies with an average surface brightness luminosity greater than 23 magnitude arcsec-2 in the r-band, making them fainter than the night sky. Thuruthipilly et al.(2023) built a machine learning (ML) pipeline able to detect them in the Dark Energy Survey(DES) Data Release 1 using the optimal bands(r/g/z-bands), finding thousands of new...

40. [INVITED] ML methods in Cosmology

Angus H. Wright

03/09/2026, 09:30

Cosmology

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

Diogo Castelão

03/09/2026, 10:10

Cosmology

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

Cosmology

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

Cosmology

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

Cosmology

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

Cosmology

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...

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

Ethan Tregidga

03/09/2026, 12:10

Flash Talks

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 (remote)

Anumanchi Agastya Sai Ram Likhit

03/09/2026, 12:15

Flash Talks

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 (remote)

Tirthankar De

03/09/2026, 12:20

Flash Talks

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

Flash Talks

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...

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

Benjamin Csizi

03/09/2026, 14:00

Cosmology

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

Cosmology

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

Cosmology

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 (remote)

Boris Khanikati

03/09/2026, 15:00

Cosmology

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

Cosmology

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

Cosmology

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...

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

Flash Talks

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

Flash Talks

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...

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

Mehdi Noor

04/09/2026, 09:00

Cosmology

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

Cosmology

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

Cosmology

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...

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

Lorenzo Branca

04/09/2026, 10:00

Flash Talks

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...

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

Francisco Villaescusa-Navarro (Flatiron Institute / Princeton University)

04/09/2026, 10:05

Generative AI

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

Morgan Fouesneau

04/09/2026, 11:10

Generative AI

30. Multi-Agent AI Systems for Astrophysics and Cosmology

Andrius Tamosiunas

04/09/2026, 11:50

Generative AI

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, 12:10

Generative AI

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, 14:00

Generative AI

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, 14:20

Generative AI

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...

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

Qitong Anabel Tan

04/09/2026, 14:40

Generative AI

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...

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

Gabriel Jaimes-Illanes

04/09/2026, 15:00

Flash Talks

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

Flash Talks

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

Flash Talks

(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 (remote)

Yagyasha Rastogi

04/09/2026, 15:15

Flash Talks

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

Flash Talks

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

Flash Talks

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...

67. Leveraging Transformer Architectures for Scalable Exoplanet Transit Inference

Vikash Singh

Flash Talks

Current exoplanet detection relies heavily on iterative sampling and box-searching, methods that face significant scaling challenges with the massive data volumes expected from next-generation surveys. This presentation introduces an exploratory concept: adapting Large Language Model (LLM) architectures to treat photometric light curves as "textual" sequences. By utilizing self-attention...

66. SELDON: A Foundation Model for Transients

Jack O'Brien

Time Domain And Transients

Next generation transient discovery surveys require accurate real-time analysis of evolving transients for followup prioritization from other surveys. SELDON (Supernova Explosions Learned by Deep ODE Networks) is a transient foundation model designed with this capability in mind, and more. Our model not only provides classifications and forecasts for the future evolution of transient...

26. TO BE CONFIRMED

Flash Talks