Toward porting Astrophysics Visual Analytics Services to the European Open Science Cloud

TL;DR

This paper discusses integrating visual analytics services into the European Open Science Cloud to support astrophysics research, focusing on data visualization, processing, and machine learning for large-scale data analysis.

Contribution

It presents ongoing efforts to implement visual analytics tools within EOSC tailored for astrophysics, emphasizing data management, visualization, and ML techniques.

Findings

Development of visualization tools for astrophysics data

Integration of machine learning for structure detection

Support for FAIR principles in data processing

Abstract

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps.