Astronomy has entered the era of big data with the advent of next-generation ground-based and space-borne instruments able to carry out all-sky surveys from gamma-rays to radio bands. In this landscape, Artificial Intelligence (AI) is a key resource, as it enables the efficient analysis and interpretation of vast amounts of complex astronomical data that traditional methods struggle to handle. By leveraging machine learning (ML) and deep learning (DL) techniques, AI can accelerate discoveries, uncover hidden patterns, and extract meaningful insights from the massive amount of data produced by modern telescopes, simulations, and space missions.
Over the last decade, INAF has been deeply involved in and has made significant investments in the design and development of large astronomical projects, such as SKA and precursors, CTA, Euclid, Vera-Rubin, ELT, Gaia, and others. In this context, promoting and enhancing AI skills within the INAF community is essential not only for addressing the data analysis, scientific and technological challenges that arise in these projects but also for strengthening INAF’s role in these international collaborations.
Indeed, during the visits that USC8 conducted in recent months to various INAF sites, a strong interest in AI techniques emerged from the community. This interest led us to propose a new thematic group dedicated to this topic.
The group was formed following a kickoff round table held on July 12, 2024, during the ML4ASTRO2 conference (https://indico.ict.inaf.it/event/2690/), where interested INAF researchers gathered to discuss ideas and proposals regarding the group’s objectives and development.
Objectives
The primary goal of the “AI in Astronomy” thematic group is to facilitate the sharing of knowledge, experiences and best practices, provide support, and promote collaboration among INAF researchers who are currently using or plan to use ML/DL techniques in their research. Additionally, the group aims to foster synergies between various astrophysical topics where a specific ML/DL approach could serve as a connecting link.
By pooling their expertise, the group aims to help members identify suitable solutions for specific problems, and tackle the challenges of applying AI models in astronomy – from data preparation and preprocessing to model development, training, testing, optimization, and deployment.
The group can also encourage synergies between various INAF sites for various scopes, including for example:
- collaborative development of software or datasets;
- investigating emerging technologies;
- producing joint publications;
- organizing periodic workshops, schools, and conferences to promote knowledge sharing and training within the community;
- building a critical mass to prepare proposals for EU and national grants, or INAF projects (e.g. Large, Technology, or Data Analysis grants);
- discussing and advocating for recruitment opportunities, career paths, and progression – from PhD to staff positions – within INAF for AI specialists and data analysts.
Group promoters
Simone Riggi (INAF-OACT)
Giuliano Taffoni (INAF-OATS)
Members
| INAF Institute | Members |
| INAF OATs | Giuliano Taffoni |
| INAF OA Brera | Marco Landoni |
| INAF OACT | Simone Riggi Federico Incardona Cristobal Bordiu Ugo Becciani Filomena Bufano Eva Sciacca Francesco Schillirò Thomas Cecconello Mauro Imbrosciano |
| IRA | Nicolò Antonietti Chiara Stuardi |
| INAF OAA | Alessio Turchi Guido Agapito Giuseppe Germano Sacco Fabio Rossi Francesco Belfiore |
| INAF OAS | Nicolò Parmiggiani Ambra Di Piano |
| INAF OAPa | Giovanni Peres |
| INAF IASF-Mi | Lorenzo Piga Eleonora Villa Susanna Bisogni |
| INAF IASF-Pa | Antonio Pagliaro |
| UNICt | Giuseppe Puglisi |
| UNINa | Nicola Napolitano |
Contact info
Everyone interested, including those who are not experts in the field, is more than welcome to join the group, either as participants or as organizers of initiatives and events.
To join the group or for further information, please contact us at the following mail address:
usc8-ai@inaf.it