arXiv subjects

Susana Sánchez-Expósito

Publications and source records attributed to Susana Sánchez-Expósito.

8 recordsLinked to original sources

arXiv2026

Towards the exploration of astrophysical datacubes through volumetric rendering within CARTA

Data products from astrophysical observations may contain three or more dimensions: most commonly two spatial dimensions and a third spectral dimension, and possibly additional axes such as polarisation or time. Visualising three-dimensional image data is particularly useful for analysing the kinematics and evolution of various objects; however, the large scale of data produced by next-generation observatories and the complexity of displaying 3D data on a 2D screen make visualisation challenging. In this article, we present a scalable and interactive 3D visualisation approach within the astronomy software CARTA. We implemented a prototype 3D rendering widget with ThreeJS and known techniques to improve efficiency (e.g. compression, downsampling) and found that large datacubes up to 128 GB can be visualised locally with a loading time of a few minutes. We analysed the performance in the frontend and in the backend, finding that the bottleneck of our implementation lies in the frontend. This is due to storing the data in a single array in the frontend, unlike in the backend, where it is accessed and sent in blocks. This study paves the way for making 3D visualisation of large datacubes available to the community by implementing a 3D rendering widget in a future release of CARTA.

astro-ph.IM↗

arXiv2026

The Nearest Galactic Nucleus: Studying the Galactic Centre with SKA-Mid

The Galactic Centre is the nearest nucleus of a galaxy and the most extreme environment that we can observe down to physical scales of a few hundred astronomical units. There is no other region in the Milky Way that can match its unique characteristics, such as its stellar density, turbulence and temperature of the interstellar medium, strong large scale magnetic field, concentration of stellar remnants, or mean star formation rate. The Galactic Centre is a unique target to understand the physics of galactic nuclei and study a large number of rare objects, such as extremely massive stars and stellar remnants, at a well-defined distance. The Galactic Centre has been and is being studied intensively with the most advanced facilities. In this chapter, we advocate for a large-area, multi-wavelength continuum survey with the Square Kilometre Array of an area of about 2.0deg x 0.4deg (~290pc x 60pc), centred on the massive black hole Sagittarius A* and for repeated deep observations of the nuclear star cluster over a decade, which will allow the community to address multiple science problems with single dataset.

astro-ph.GA↗

arXiv2026

Bringing Computation to the data: Interoperable serverless function execution for astrophysical data analysis in the SRCNet

Serverless computing is a paradigm in which the underlying infrastructure is fully managed by the provider, enabling applications and services to be executed with elastic resource provisioning and minimal operational overhead. A core model within this paradigm is Function-as-a-Service (FaaS), where lightweight functions are deployed and triggered on demand, scaling seamlessly with workload. FaaS offers flexibility, cost-effectiveness, and fine-grained scalability, qualities particularly relevant for large-scale scientific infrastructures where data volumes are too large to centralise and computation must increasingly occur close to the data. The Square Kilometre Array Observatory (SKAO) exemplifies this challenge. Once operational, it will generate about 700~PB of data products annually, distributed across the SKA Regional Centre Network (SRCNet), a federation of international centres providing storage, computing, and analysis services. In such a context, FaaS offers a mechanism to bring computation to the data. We studied the principles of serverless and FaaS computing and explored their application to radio astronomy workflows. Representative functions for astrophysical data analysis were developed and deployed, including micro-functions derived from existing libraries and wrappers around domain-specific applications. In particular, a Gaussian convolution function was implemented and integrated within the SRCNet ecosystem. The use case demonstrates that FaaS can be embedded into the existing SRCNet ecosystem of services, allowing functions to run directly at sites where data replicas are stored. This reduces latency, minimises transfers, and improves efficiency, aligning with federated, data-proximate computation. The results show that serverless models provide a scalable and efficient pathway to address the data volumes of the SKA era.

cs.DC↗

arXiv2026

Bringing computation to the data: A MOEA-driven approach for optimising data processing in the context of the SKA and SRCNet

The Square Kilometre Array (SKA) will generate unprecedented data volumes, making efficient data processing a critical challenge. Within this context, the SKA Regional Centres Network (SRCNet) must operate in a near-exascale environment where traditional data-centric computing models based on moving large datasets to centralised resources are no longer viable due to network and storage bottlenecks. To address this limitation, this work proposes a shift towards distributed and in-situ computing, where computation is moved closer to the data. We explore the integration of Function-as-a-Service (FaaS) with an intelligent decision-making entity based on Evolutionary Algorithms (EAs) to optimise data-intensive workflows within SRCNet. FaaS enables lightweight and modular function execution near data sources while abstracting infrastructure management. The proposed decision-making entity employs Multi-Objective Evolutionary Algorithms (MOEAs) to explore near-optimal execution plans considering execution time and energy consumption, together with constraints related to data location and transfer costs. This work establishes a baseline framework for efficient and cost-aware computation-to-data strategies within the SRCNet architecture.

cs.DC↗

arXiv2025

Classification of HI Galaxy Profiles Using Unsupervised Learning and Convolutional Neural Networks: A Comparative Analysis and Methodological Cases of Studies

Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into interactions, galactic structure, and star formation processes. With new radio instruments, the volume and complexity of data is increasing. To analyze and classify integrated HI spectral profiles in a efficient way, this work presents a framework that integrates Machine Learning techniques, combining unsupervised methods and CNNs. To this end, we apply our framework to a selected subsample of 318 spectral HI profiles of the CIG and 30.780 profiles from the Arecibo Legacy Fast ALFA Survey catalogue. Data pre-processing involved the Busyfit package and iterative fitting with polynomial, Gaussian, and double-Lorentzian models. Clustering methods, including K-means, spectral clustering, DBSCAN, and agglomerative clustering, were used for feature extraction and to bootstrap classification we applied K-NN, SVM, and Random Forest classifiers, optimizing accuracy with CNN. Additionally, we introduced a 2D model of the profiles to enhance classification by adding dimensionality to the data. Three 2D models were generated based on transformations and normalised versions to quantify the level of asymmetry. These methods were tested in a previous analytical classification study conducted by the Analysis of the Interstellar Medium in Isolated Galaxies group. This approach enhances classification accuracy and aims to establish a methodology that could be applied to data analysis in future surveys conducted with the Square Kilometre Array (SKA), currently under construction. All materials, code, and models have been made publicly available in an open-access repository, adhering to FAIR principles.

astro-ph.GA↗

arXiv2024

Photometry and kinematics of dwarf galaxies from the Apertif HI survey

Context. Understanding the dwarf galaxy population in low density environments is crucial for testing the LCDM cosmological model. The increase in diversity towards low mass galaxies is seen as an increase in the scatter of scaling relations such as the stellar mass-size and the baryonic Tully-Fisher relation (BTFR), and is also demonstrated by recent in-depth studies of an extreme subclass of dwarf galaxies of low surface brightness, but large physical sizes, called ultra-diffuse galaxies (UDGs). Aims. We select galaxies from the Apertif HI survey, and apply a constraint on their i-band absolute magnitude to exclude high mass systems. The sample consists of 24 galaxies, and span HI mass ranges of 8.6 < log ($M_{HI}/M_{Sun}$) < 9.7 and stellar mass range of 8.0 < log ($M_*/M_{Sun}$) < 9.7 (with only three galaxies having log ($M_*/M_{Sun}$) > 9). Methods. We determine the geometrical parameters of the HI and stellar discs, build kinematic models from the HI data using 3DBarolo, and extract surface brightness profiles in g-, r- and i-band from the Pan-STARRS 1 photometric survey. Results. We find that, at fixed stellar mass, our HI selected dwarfs have larger optical effective radii than isolated, optically-selected dwarfs from the literature. We find misalignments between the optical and HI morphologies for some of our sample. For most of our galaxies, we use the HI morphology to determine their kinematics, and we stress that deep optical observations are needed to trace the underlying stellar discs. Standard dwarfs in our sample follow the same BTFR of high-mass galaxies, whereas UDGs are slightly offset towards lower rotational velocities, in qualitative agreement with results from previous studies. Finally, our sample features a fraction (25%) of dwarf galaxies in pairs that is significantly larger with respect to previous estimates based on optical spectroscopic data.

astro-ph.GA↗

arXiv2024

Integration of storage endpoints into a Rucio data lake, as an activity to prototype a SKA Regional Centres Network

The Square Kilometre Array (SKA) infrastructure will consist of two radio telescopes that will be the most sensitive telescopes on Earth. The SKA community will have to process and manage near exascale data, which will be a technical challenge for the coming years. In this respect, the SKA Global Network of Regional Centres plays a key role in data distribution and management. The SRCNet will provide distributed computing and data storage capacity, as well as other important services for the network. Within the SRCNet, several teams have been set up for the research, design and development of 5 prototypes. One of these prototypes is related to data management and distribution, where a data lake has been deployed using Rucio. In this paper we focus on the tasks performed by several of the teams to deploy new storage endpoints within the SKAO data lake. In particular, we will describe the steps and deployment instructions for the services required to provide the Rucio data lake with a new Rucio Storage Element based on StoRM and WebDAV within the Spanish SRC prototype.

astro-ph.IM↗

arXiv2024

An approach to provide serverless scientific pipelines within the context of SKA

Function-as-a-Service (FaaS) is a type of serverless computing that allows developers to write and deploy code as individual functions, which can be triggered by specific events or requests. FaaS platforms automatically manage the underlying infrastructure, scaling it up or down as needed, being highly scalable, cost-effective and offering a high level of abstraction. Prototypes being developed within the SKA Regional Center Network (SRCNet) are exploring models for data distribution, software delivery and distributed computing with the goal of moving and executing computation to where the data is. Since SKA will be the largest data producer on the planet, it will be necessary to distribute this massive volume of data to the SRCNet nodes that will serve as a hub for computing and analysis operations on the closest data. Within this context, in this work we want to validate the feasibility of designing and deploying functions and applications commonly used in radio interferometry workflows within a FaaS platform to demonstrate the value of this computing model as an alternative to explore for data processing in the distributed nodes of the SRCNet. We have analyzed several FaaS platforms and successfully deployed one of them, where we have imported several functions using two different methods: microfunctions from the CASA framework, which are written in Python code, and highly specific native applications like wsclean. Therefore, we have designed a simple catalogue that can be easily scaled to provide all the key features of FaaS in highly distributed environments using orchestrators, as well as having the ability to integrate them with workflows or APIs. This paper contributes to the ongoing discussion of the potential of FaaS models for scientific data processing, particularly in the context of large-scale, distributed projects such as SKA.

cs.DC↗