[{"data":1,"prerenderedAt":30},["ShallowReactive",2],{"nr-en-claude-science-uv-sky-map":3},{"slug":4,"title":5,"dek":6,"date":7,"time":8,"publishedAt":9,"updated":10,"updatedAt":10,"dateFmt":11,"updatedFmt":10,"kind":12,"tier":13,"author":14,"authorName":15,"topics":16,"tracker":22,"trackerLabel":23,"headlineStat":24,"image":25,"ogImage":26,"imageAlt":5,"csv":10,"minutes":27,"words":28,"html":29},"claude-science-uv-sky-map","Anthropic Uses Claude Science to Create First Complete UV Sky Map","Astrophysicist Brice Ménard from Johns Hopkins University leveraged Claude Science to produce the first complete ultraviolet map of the sky – a breakthrough demonstrating how AI closes scientific gaps.","2026-10-09","08:12","2026-10-09T08:12:00+02:00","","October 9, 2026","news","standard","ideal-syka","Ideal Syka",[17,18,19,20,21],"Claude Science","Astrophysics","AI Research","Space Observation","Data Analysis","\u002Fstand-der-ki","AI Progress","Approximately one-third of the UV sky map was predicted by Claude Science","\u002Fnewsroom\u002Fimg\u002Fclaude-science-uv-sky-map.webp","\u002Fog-nr\u002Fclaude-science-uv-sky-map.en.png",3,549,"\u003Cp>Claude Science has just accomplished what was previously too demanding: creating a complete map of the sky in ultraviolet light. Brice Ménard, an astrophysicist at Johns Hopkins University and researcher at Anthropic, used the AI system to predict approximately one-third of the map – particularly large sections of the galactic plane – filling gaps left by previous space telescopes.\u003C\u002Fp>\n\u003Ch2>The essentials\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Cstrong>First complete UV sky map\u003C\u002Fstrong> created using Claude Science; roughly \u003Cstrong>one-third of the map was predicted by the AI\u003C\u002Fstrong>\u003C\u002Fli>\n\u003Cli>\u003Cstrong>NASA's GALEX mission\u003C\u002Fstrong> (2003–2013) observed only about \u003Cstrong>two-thirds of the sky\u003C\u002Fstrong> and deliberately skipped bright stars and the galactic plane\u003C\u002Fli>\n\u003Cli>Ménard's work typically required \u003Cstrong>weeks of manual calibration and analysis\u003C\u002Fstrong> – Claude made this task feasible\u003C\u002Fli>\n\u003Cli>The map will serve as an \u003Cstrong>educational tool\u003C\u002Fstrong> for astrophysics students, revealing the structure of the Milky Way in UV light\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2>Why a complete UV map didn't exist\u003C\u002Fh2>\n\u003Cp>Ultraviolet light is absorbed by Earth's atmosphere – requiring space telescopes to observe it. The largest data source was \u003Cstrong>NASA's GALEX mission\u003C\u002Fstrong>, which ran from 2003 to 2013 and imaged roughly two-thirds of the sky in approximately 38,000 separate observations. However, GALEX deliberately avoided very bright stars – especially in the galactic plane where stars are most densely packed – to protect the satellite's sensitive detectors from damage.\u003C\u002Fp>\n\u003Cp>Other space telescopes like \u003Cstrong>NASA's Swift\u003C\u002Fstrong> and \u003Cstrong>South Korea's FIMS\u002FSPEAR\u003C\u002Fstrong> contributed additional data, yet even combined, significant gaps remained. Statistical methods could fill these gaps, but doing so properly demands \u003Cstrong>weeks of painstaking work\u003C\u002Fstrong> with pixel-level calibration and repeated analyses – effort astrophysicists typically defer in favor of more urgent research.\u003C\u002Fp>\n\u003Ch2>Claude makes the impossible practical\u003C\u002Fh2>\n\u003Cblockquote>\n\u003Cp>"With Claude, it has become easier to tackle such lower-priority work," Ménard explains.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Cp>This summer, Ménard set out to create the missing map using Claude Science. The instructions were simple to state but difficult to execute: gather all available UV data, calibrate it, statistically fill the gaps, and produce a complete map with uncertainty estimates.\u003C\u002Fp>\n\u003Cp>The result: an interactive map of the entire sky with the galactic center in the middle, combining \u003Cstrong>Far-UV (154 nm) and Near-UV (232 nm)\u003C\u002Fstrong>. Additional layers mark each pixel as "measured" or "predicted" and provide uncertainty estimates. This eliminates the need to apologize in the lecture hall – Ménard can now show his students how UV light reveals dust illuminated by starlight: from clouds around young stars to rings left by stellar explosions.\u003C\u002Fp>\n\u003Ch2>What this means for science\u003C\u002Fh2>\n\u003Cp>The map is more than an educational tool. It illustrates a broader pattern: many scientific fields have backlogs of projects that would explain key concepts or assist other researchers – but never rise high enough on anyone's priority list because manual work is too demanding. Claude Science makes such projects suddenly feasible without researchers sacrificing months of time.\u003C\u002Fp>\n\u003Cp>For German research institutions and universities, this could mean reconsidering previously shelved data preparation and analysis projects. Frontier AI systems like Claude Science might free up resources – but only if institutions build the necessary infrastructure and expertise to use such tools effectively. Simultaneously, questions arise about reproducibility and validation: how transparent must AI-assisted scientific predictions be to be considered reliable?\u003C\u002Fp>\n\u003Ch2>Sources\u003C\u002Fh2>\n\u003Cul>\n\u003Cli>\u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fresearch\u002Fthe-missing-map-of-the-sky\">Anthropic\u003C\u002Fa>\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Cp>\u003Cem>Editorially owned by \u003Ca href=\"\u002Fen\u002Fautor\u002Fideal-syka\">Ideal Syka\u003C\u002Fa>. Sources and method: \u003Ca href=\"\u002Fen\u002Fredaktion\">Newsroom & method\u003C\u002Fa>. Tips and corrections: \u003Ca href=\"mailto:ai@i6eal.de\">ai@i6eal.de\u003C\u002Fa>.\u003C\u002Fem>\u003C\u002Fp>\n",1791531367052]