arXiv subjects

Elena Gavagnin

Publications and source records attributed to Elena Gavagnin.

7 recordsLinked to original sources

arXiv2026

The Role of Artificial Intelligence in the SKA Era

The Square Kilometre Array Observatory (SKAO) will usher in an era of unprecedented data complexity and scientific opportunity in radio astronomy, producing petabyte-scale datasets and terabit-per-second streams that challenge traditional analysis paradigms. Artificial Intelligence (AI) stands at the forefront of this transformation, offering scalable, adaptive solutions to the most pressing problems in radio astronomy and astrophysics. This chapter explores the pivotal role of AI in the SKA era, from real-time operations to scientific discovery. We examine how deep learning models enable automated source detection, radio-frequency interference mitigation, anomaly detection, and parameter inference, while generative approaches accelerate sky simulations, calibration, and imaging. Reinforcement learning promises dynamic scheduling and autonomous system control, and federated learning could address the distributed nature of SKA data. Beyond performance, we emphasize the necessity of explainability, uncertainty quantification, and physics-informed inductive biases to ensure scientific integrity. By mapping SKAO's core challenges - data volume, complexity, and interpretability - onto modern AI methodologies, we review how deep learning, self-supervised frameworks, and probabilistic models can unlock new frontiers in cosmology, galaxy evolution, and time-domain astrophysics. AI is not merely an automation tool for coping with scale. It is a catalyst for discovery, redefining how we observe, model, and understand the Universe.

astro-ph.IM↗

arXiv2026

Optimization of Deep Learning Models for Radio Galaxy Classification

Modern radio telescope surveys, capable of detecting billions of galaxies in wide-field surveys, have made manual morphological classification impracticable. This applies in particular when the Square Kilometre Array Observatory (SKAO) becomes operable in 2027, which is expected to close an important gap in our understanding of the Epoch of Reionization (EoR) and other areas of astrophysics. To this end, foreground objects, contaminants of the 21-cm signal, need to be identified and subtracted. Source finding and identification is thus an important albeit challenging task. We investigate the ability of AI and deep learning (DL) methods that have been previously trained on other data domains to localize and classify radio galaxies with minimal changes to their architectures. Various well-known pretrained neural network architectures for image classification and object detection are trained and fine-tuned and their performance is evaluated on a public radio galaxy dataset derived from the Radio Galaxy Zoo. A comparison between convolutional neural network (CNN)- and transformer-based algorithms is performed. The best performing architecture is systematically optimized and an uncertainty estimation is performed by means of an ensemble analysis. Radio source classification performance nearly comparable to the current leading customized models can be obtained using existing standard pretrained DL architectures, without modification and increase in complexity of the model architectures but rather adaptation of the data, by combining various transformations on replicated image channels. Using an ensemble of models can also further improve performance to over 90% accuracy, on par with top-performing models in the literature. The results can be transferred to other survey data, e.g. from the Murchison Wide-field Array (MWA), and in the future be used to study the EoR with the SKAO.

astro-ph.GA↗

arXiv2025

Galactic Alchemy: Deep Learning Map-to-Map Translation in Hydrodynamical Simulations

We present the first systematic study of multi-domain map-to-map translation in galaxy formation simulations, leveraging deep generative models to predict diverse galactic properties. Using high-resolution magneto-hydrodynamical simulation data, we compare conditional generative adversarial networks and diffusion models under unified preprocessing and evaluation, optimizing architectures and attention mechanisms for physical fidelity on galactic scales. Our approach jointly addresses seven astrophysical domains - including dark matter, gas, neutral hydrogen, stellar mass, temperature, and magnetic field strength - while introducing physics-aware evaluation metrics that quantify structural realism beyond standard computer vision measures. We demonstrate that translation difficulty correlates with physical coupling, achieving near-perfect fidelity for mappings from gas to dark matter and mappings involving astro-chemical components such as total gas to HI content, while identifying fundamental challenges in weakly constrained tasks such as gas to stellar mass mappings. Our results establish GAN-based models as competitive counterparts to state-of-the-art diffusion approaches at a fraction of the computational cost (in training and inference), paving the way for scalable, physics-aware generative frameworks for forward modelling and observational reconstruction in the SKA era.

astro-ph.GA↗

arXiv2024

PROMISE: A Framework for Developing Complex Conversational Interactions (Technical Report)

The advent of increasingly powerful language models has raised expectations for language-based interactions. However, controlling these models is a challenge, emphasizing the need to be able to investigate the feasibility and value of their application. We present PROMISE, a framework that facilitates the development of complex language-based interactions with information systems. Its use of state machine modeling concepts enables model-driven, dynamic prompt orchestration across hierarchically nested states and transitions. This improves the control of the behavior of language models and thus enables their effective and efficient use. In this technical report we show the benefits of PROMISE in the context of application scenarios within health information systems and demonstrate its ability to handle complex interactions. We also include code examples and present default user interfaces available as part of PROMISE.

cs.CL↗

arXiv2023

Divide et Impera: Multi-Transformer Architectures for Complex NLP-Tasks

The growing capabilities of transformer models pave the way for solving increasingly complex NLP tasks. A key to supporting application-specific requirements is the ability to fine-tune. However, compiling a fine-tuning dataset tailored to complex tasks is tedious and results in large datasets, limiting the ability to control transformer output. We present an approach in which complex tasks are divided into simpler subtasks. Multiple transformer models are fine-tuned to one subtask each, and lined up to accomplish the complex task. This simplifies the compilation of fine-tuning datasets and increases overall controllability. Using the example of reducing gender bias as a complex task, we demonstrate our approach and show that it performs better than using a single model.

cs.CL↗

arXiv2017

Star Cluster Formation in a Turbulent Molecular Cloud Self-Regulated by Photo-Ionisation Feedback

Most stars in the Galaxy are believed to be formed within star clusters from collapsing molecular clouds. However, the complete process of star formation, from the parent cloud to a gas-free star cluster, is still poorly understood. We perform radiation-hydrodynamical simulations of the collapse of a turbulent molecular cloud using the RAMSES-RT code. Stars are modelled using sink particles, from which we self-consistently follow the propagation of the ionising radiation. We study how different feedback models affect the gas expulsion from the cloud and how they shape the final properties of the emerging star cluster. We find that the star formation efficiency is lower for stronger feedback models. Feedback also changes the high mass end of the stellar mass function. Stronger feedback also allows the establishment of a lower density star cluster, which can maintain a virial or sub-virial state. In the absence of feedback, the star formation efficiency is very high, as well as the final stellar density. As a result, high energy close encounters make the cluster evaporate quickly. Other indicators, such as mass segregation, statistics of multiple systems and escaping stars confirm this picture. Observations of young star clusters are in best agreement with our strong feedback simulation.

astro-ph.GA↗

arXiv2016

A critical look at the merger scenario to explain multiple populations and rotation in iron-complex globular clusters

Merging has been proposed to explain multiple populations in globular clusters (GCs) where there is a spread in iron abundance (hereafter, iron-complex GCs). By means of N-body simulations, we investigate if merging is consistent with the observations of sub-populations and rotation in iron-complex GCs. The key parameters are the initial mass and density ratios of the progenitors. When densities are similar, the more massive progenitor dominates the central part of the merger remnant and the less massive progenitor forms an extended rotating population. The low-mass progenitor can become the majority population in the central regions of the merger remnant only if its initial density is higher by roughly the mass ratio. To match the radial distribution of multiple populations in two iron-complex GCs (ω Cen and NGC 1851), the less massive progenitor needs to be four times as dense as the larger one. Our merger remnants show solid-body rotation in the inner parts, becoming differential in the outer parts. Rotation velocity V and ellipticity ε are in agreement with models for oblate rotators with isotropic dispersion. We discuss several kinematic signatures of a merger with a denser lower mass progenitor that can be tested with future observations.

astro-ph.GA↗