TWiki> IVOA Web>WebPreferences>InterOpNov2021>InterOpMay2023KD (revision 8)EditAttach

Knowledge Discovery

Time: Tuesday May 09 11:00 UTC

Speaker Title Time Abstract Material
Raffaele D'Abrusco Greetings and Introduction 10'   pdf
Sandor Kruk Exploring astronomy data archives at large scales using deep learning and crowdsourcing 15'+5' The vast amount of data in astronomy archives presents an opportunity for new discoveries. Deep learning combined with crowdsourcing provides an efficient way to explore this data using. the intuition of the human brain and the processing power of machines. In the Hubble Asteroid Hunter project, we used a deep learning algorithm on Google Cloud, trained on volunteer classifications from the asteroidhunter.org Zooniverse project to search two decades of Hubble Space Telescope (HST) observations from the ESA HST archives for objects not targeted by the Hubble observations. The project, which was set up as a collaboration between Zooniverse, ESAC Science Data Center and engineers at Google, led to the discovery of 1700 asteroids (Kruk et al. 2022), including 1031 previously unknown ones (Garcia Martinet al., in prep.), 198 new strong gravitational lenses (Garvin et al. 2022), and quantified the impact of artificial satellites on HST observations (Kruk et al. 2023). In this talk, we will present the results of this project and highlight the benefits of scientifically exploiting the vast amounts of data available in astronomy data archives using novel techniques. pdf
Raffaele/Yihan Introduction to mini-session on generative AI and language models 5'   pdf
Yihan Tao Foundation models for Astronomy 5'   pdf
Rafael Galarza-Martinez Intro to Transformers 10'   pdf
Ioana Ciucă Galactic ChitChat: Using Large Language Models to Engage with Astronomy Literature 5' We showcase the capacity of the OpenAI's large language model GPT-4 for meaningful engagement with Astronomy papers using in-context prompting. We employ a distillation technique to optimise efficiency, reducing the input paper size by 50% while preserving paragraph structure and semantic integrity. The in-context model excels at providing detailed answers contextualised by related research findings by examining its responses within a multi-document context (ten distilled documents). Our investigation highlights the potential of foundation models for the astronomical community. For example, they can help researchers gain insights from astronomical literature, such as validating new scientific hypotheses or proposing novel ideas. pdf
Panel + audience Discussion - panel comprising speakers 30'   pdf
Moderator: Raffaele D'Abrusco, Notetaker: TBD, Etherpad link
Attachments Attachments
Topic attachments
I Attachment History Action Size Date Who Comment
PDFpdf KD-IG_anomalies.pdf r1 manage 34742.0 K 2021-11-03 - 13:21 RaffaeleDAbrusco  
PDFpdf Mahabal_IVOA_20211103.pdf r1 manage 59.2 K 2021-11-03 - 20:17 RaffaeleDAbrusco  
PDFpdf skoda-bayesian-redshift.pdf r1 manage 1812.3 K 2021-11-03 - 20:14 RaffaeleDAbrusco  
PDFpdf slides_session_KDIG_IVOA_2021FallInterOp.pdf r1 manage 193.3 K 2021-11-04 - 18:12 RaffaeleDAbrusco  
Edit | Attach | Watch | Print version | History: r22 | r10 < r9 < r8 < r7 | Backlinks | Raw View | Raw edit | More topic actions...
Topic revision: r8 - 2023-04-26 - RaffaeleDAbrusco
 
This site is powered by the TWiki collaboration platform Powered by Perl This site is powered by the TWiki collaboration platformCopyright © 2008-2025 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback