{"id":1100,"date":"2024-09-14T11:46:20","date_gmt":"2024-09-14T09:46:20","guid":{"rendered":"https:\/\/uscc.inaf.it\/?page_id=1100"},"modified":"2026-03-27T11:12:57","modified_gmt":"2026-03-27T10:12:57","slug":"ai-in-astronomy","status":"publish","type":"page","link":"https:\/\/uscc.inaf.it\/en\/tematic-groups\/ai-in-astronomy\/","title":{"rendered":"AI for astronomy"},"content":{"rendered":"

Astronomy has entered the era of Big Data<\/strong> 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)<\/strong> 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)<\/strong> and Deep Learning (DL)<\/strong>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.<\/p>\n\n\n\n

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\u2019s role in these international collaborations.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

The group was formed following a kickoff round table held on July 12, 2024, during the ML4ASTRO2<\/a><\/strong>where interested INAF researchers gathered to discuss ideas and proposals regarding the group\u2019s objectives and development.
<\/p>\n\n\n\n

Objectives<\/em><\/p>\n\n\n\n

The primary goal of the \u201cAI in Astronomy\u201d 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.<\/p>\n\n\n\n

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 \u2013 from data preparation and preprocessing to model development, training, testing, optimization, and deployment.<\/p>\n\n\n\n

The group can also encourage synergies between various INAF sites for various scopes, including for example:<\/p>\n\n\n\n

\n

<\/path><\/svg><\/span> collaborative development of software or datasets;<\/p>\n\n\n\n

<\/path><\/svg><\/span> investigating emerging technologies;<\/p>\n\n\n\n

<\/path><\/svg><\/span> producing joint publications;<\/p>\n\n\n\n

<\/path><\/svg><\/span> organizing periodic workshops, schools, and conferences to promote knowledge sharing and training within the community;<\/p>\n\n\n\n

<\/path><\/svg><\/span> building a critical mass to prepare proposals for EU and national grants, or INAF projects (e.g. Large, Technology, or Data Analysis grants);<\/p>\n\n\n\n

<\/path><\/svg><\/span> discussing and advocating for recruitment opportunities, career paths, and progression \u2013 from PhD to staff positions \u2013 within INAF for AI specialists and data analysts.<\/p>\n<\/div>\n\n\n\n

Group promoters<\/em><\/p>\n\n\n\n

Simone Riggi (INAF-OACT)
Giuliano Taffoni (INAF-OATS)
<\/p>\n\n\n\n

Members<\/em><\/p>\n\n\n\n

INAF Institute<\/strong><\/td>Members<\/strong><\/td><\/tr>
INAF OATs<\/td>Giuliano Taffoni<\/td><\/tr>
INAF OA Brera<\/td>Marco Landoni<\/td><\/tr>
INAF OACT<\/td>Simone Riggi
Federico Incardona
Cristobal Bordiu
Ugo Becciani
Filomena Bufano
Eva Sciacca
Francesco Schillir\u00f2
Thomas Cecconello
Mauro Imbrosciano
Marco Sortino
Farida Farina<\/td><\/tr>
IRA<\/td>Nicol\u00f2 Antonietti
Chiara Stuardi
Claudio Gheller<\/td><\/tr>
INAF OAA<\/td>Alessio Turchi
Guido Agapito
Giuseppe Germano Sacco
Fabio Rossi
Francesco Belfiore<\/td><\/tr>
INAF OAS<\/td>Nicol\u00f2 Parmiggiani
Ambra Di Piano
Andrea Bulgarelli
Cristiano Fanelli
Lorenzo Monti
Tatiana Muraveva<\/td><\/tr>
INAF OAPa<\/td>Giovanni Peres<\/td><\/tr>
INAF IASF-Mi
<\/td>		Lorenzo Piga
Eleonora Villa
Susanna Bisogni
Adriana Gargiulo
Carmelita Carbone 
Mario Pasquato<\/td><\/tr>
INAF IASF-Pa<\/td>Antonio Pagliaro<\/td><\/tr>
UNICt<\/td>Giuseppe Puglisi<\/td><\/tr>
UNINa<\/td>Nicola Napolitano<\/td><\/tr>
INAF Astronomical Observatory of Capodimonte<\/td>Stefano Cavuoti 
Giuseppe Riccio
Giuseppe Angora 
Ylenia Maruccia 
Giuseppe Sarracino<\/td><\/tr>
INAF Osservatorio d\u2019Abruzzo<\/td>Stefano Di Frischia<\/td><\/tr>
OA Cagliari<\/td>Alessandro Cabras<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

Contact info<\/em><\/p>\n\n\n\n

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.<\/p>\n\n\n\n

To join the group or for further information, please contact us at the following mail address:<\/p>\n\n\n\n

usc8-ai@inaf.it<\/strong><\/p>\n\n\n\n

<\/div>","protected":false},"excerpt":{"rendered":"

L’astronomia \u00e8 entrata nell’era dei Big Data grazie all’avvento di strumenti di nuova generazione, sia terrestri che spaziali, in grado di effettuare survey dell’intera volta celeste dai raggi gamma alle bande radio. In questo scenario, l’Intelligenza Artificiale (AI) rappresenta una risorsa chiave, poich\u00e9 consente l’analisi e l’interpretazione efficiente di vaste quantit\u00e0 di dati astronomici complessi […]<\/p>\n","protected":false},"author":10,"featured_media":2101,"parent":447,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"uscc-pages","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[35],"class_list":["post-1100","page","type-page","status-publish","has-post-thumbnail","hentry","tag-pagina-completata"],"acf":[],"_links":{"self":[{"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/pages\/1100","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/users\/10"}],"replies":[{"embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/comments?post=1100"}],"version-history":[{"count":8,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/pages\/1100\/revisions"}],"predecessor-version":[{"id":2450,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/pages\/1100\/revisions\/2450"}],"up":[{"embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/pages\/447"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/media\/2101"}],"wp:attachment":[{"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/media?parent=1100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/categories?post=1100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uscc.inaf.it\/en\/wp-json\/wp\/v2\/tags?post=1100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}