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UID:pretalx-adass2026-RHQVJB@pretalx.adass.org
DTSTART;TZID=AWST:20261103T144500
DTEND;TZID=AWST:20261103T150000
DESCRIPTION:Modern radio continuum surveys such as ASKAP's Evolutionary Map
  of the Universe (EMU) produce catalogues of tens of millions of sources\,
  far exceeding what manually curated pipelines can process. In this talk\,
  I will present two complementary systems addressing this. RG-CAT uses an 
 object-detection machine-learning model as its backbone to detect and clas
 sify radio galaxies — including complex\, extended morphologies — dire
 ctly from survey imaging\, feeding into cataloguing scripts that produce s
 cience-ready outputs. EMUSE (the Evolutionary Map of the Universe Search E
 ngine) uses a multimodal model to generate and search through image embedd
 ings\, making survey catalogues discoverable by the wider research communi
 ty. These are deployed as containerised\, cloud-native services\, illustra
 ting how machine learning can serve as the backbone of scalable\, end-to-e
 nd research pipelines.
DTSTAMP:20261001T111419Z
LOCATION:Banquet Hall
SUMMARY:From Detection to Discovery: ML Pipelines for the Radio Sky Surveys
  - Nikhel Gupta
URL:https://pretalx.adass.org/adass2026/talk/RHQVJB/
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