arXiv subjects

Julián Garrido-Sánchez

Publications and source records attributed to Julián Garrido-Sánchez.

2 recordsLinked to original sources

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↗

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↗