BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//pretalx.adass.org//adass2026//speaker//UMDVNL BEGIN:VTIMEZONE TZID:AWST BEGIN:STANDARD DTSTART:20000101T000000 RRULE:FREQ=YEARLY;BYMONTH=1;UNTIL=20051231T160000Z TZNAME:AWST TZOFFSETFROM:+0800 TZOFFSETTO:+0800 END:STANDARD BEGIN:STANDARD DTSTART:20070325T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:AWST TZOFFSETFROM:+0900 TZOFFSETTO:+0800 END:STANDARD BEGIN:DAYLIGHT DTSTART:20071028T030000 RRULE:FREQ=YEARLY;BYDAY=4SU;BYMONTH=10;UNTIL=20081025T190000Z TZNAME:AWDT TZOFFSETFROM:+0800 TZOFFSETTO:+0900 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-adass2026-QG8EJW@pretalx.adass.org DTSTART;TZID=AWST:20261102T140000 DTEND;TZID=AWST:20261102T141500 DESCRIPTION:The rapid adoption of generative AI is transforming the way sci entists interact with astronomy science archives and platforms. However\, the growing dependence on large foundation models raises important questio ns about sustainability\, cost\, and long-term operational viability. In t he Data Science and Archives Division at the European Space Astronomy Cent re (ESAC)\, we are exploring different ways to use AI to support our users . Among others\, we are exploring how smaller open source language models combined with retrieval-augmented generation (RAG) and domain-specific too ling can enhance astronomical data discovery while minimizing computationa l overhead. Rather than treating state of the art frontier models as the d efault solution\, we are investigating if smaller models can be effectivel y deployed for many archive-support tasks with considerably lower infrastr ucture requirements.\n\nThe ESAC astronomy science archives present a part icularly attractive use case for this approach. In legacy missions\, the n umber of specialists available to support users decreases with time\, whil e the scientific value of these missions remains high for decades. By comb ining lightweight large language models (LLM) with RAG pipelines built fro m mission documentation\, archive interfaces user guides\, and selected sc ientific publications\, it is possible to provide conversational interface s that preserve mission knowledge that can be used by scientists to discov er and use relevant datasets. Similarly\, for missions in operations\, suc h as Euclid\, AI assistants could help scientists navigate its complex dat a model\, learn the details of hundreds of distinct data products\, and ge nerate ADQL example queries and notebooks.\n\nWe argue that this approach could complement other alternatives and represents a more sustainable path for scientific archives. Running open-source LLMs with “only” a few b illion parameters on local GPU infrastructure would allow institutions to reuse existing resources\, reduce dependence on commercial AI subscription s\, and limit the environmental and financial costs associated with large- scale cloud inference. This approach also maximizes the value of open-sour ce models whose development has already required significant community inv estment. In this presentation we will showcase some of the experimental wo rk we are doing at ESAC using open source models running on our local infr astructure\, for example\, enhancing the existing ESASky chatbot with gene rative AI capabilities and building RAG pipelines for mission documentatio n and archival data discovery. DTSTAMP:20261015T123825Z LOCATION:Banquet Hall SUMMARY:Sustainable AI for Astronomical Archival Data Discovery in the Peta byte Era - Marcos López-Caniego URL:https://pretalx.adass.org/adass2026/talk/QG8EJW/ END:VEVENT BEGIN:VEVENT UID:pretalx-adass2026-WP89MK@pretalx.adass.org DTSTART;TZID=AWST:20261104T173000 DTEND;TZID=AWST:20261104T183000 DESCRIPTION:Astronomy is entering an era in which several major facilities are simultaneously operating\, or standing up\, science platforms designed to deliver petabyte-scale data and computing to their communities. As mis sions and observatories move from data collection to data delivery at scal e\, they are independently converging on similar architectural choices — and independently running into similar operational challenges — around federated access\, compute-to-data\, authentication\, and long-term sustai nability. This BoF is proposed by confirmed panelists from Euclid/ESA Data labs\, Vera Rubin Observatory\, the SKA Regional Centre Network (SRCNet)\, Roman Telescope\, CADC/CANFAR\, NASA/Fornax and LOFAR. Other platforms ar e invited to join the discussion to compare architectures\, operational le ssons\, and user-facing services for large-scale science platforms and wor kflows.\n\nWith Euclid's Data Release 1 (DR1)\, a full year of Vera Rubin Observatory operations\, the launch of the Roman Space Telescope\, and SKA Low's science verification getting underway next year\, the timing is ide al to take stock of what's working\, what isn't\, and where the community could converge — on topics such as federated data access\, compute-to-da ta models\, interoperability standards\, and the balance between bespoke p latform development and shared infrastructure. Panellists will open the se ssion with a short position statement on how their platform is architected and operated today\, before the floor opens to a moderated discussion aim ed at surfacing common challenges and concrete opportunities for collabora tion across observatories. DTSTAMP:20261015T123825Z LOCATION:Seminar Room 1 SUMMARY:Science Platforms at Scale: Developing Data Services in the Petabyt e Era - Javier Ballester Arenas\, Yan Grange\, Marcos López-Caniego\, San dor Kruk\, Jesus Salgado\, Christophe Arviset\, Michele Delli Veneri\, Ros ie Bolton\, William O Mullane\, Carmen Toribio\, Brian Major URL:https://pretalx.adass.org/adass2026/talk/WP89MK/ END:VEVENT END:VCALENDAR