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SUMMARY:Data validation through anomaly detection for Gaia DR4 - Sireesha 
 Chamarthi (IoA / CASU)
DTSTART:20241125T160000Z
DTEND:20241125T170000Z
UID:TALK223057@talks.cam.ac.uk
CONTACT:65128
DESCRIPTION:A crucial step in a data release is performing data validation
  to distinguish true scientific anomalies from those caused by instrument 
 or calibration errors. Gaia Data Release 4 (DR4) presents significant comp
 utational and methodological challenges due to its vast\, high-dimensional
  datasets. One of the aspects of validation process is anomaly detection w
 ithin the photometry data\, which involves identifying deviations from exp
 ected patterns in time series data. In this talk\, I will first address th
 e broader data validation problem for epoch photometry in Gaia DR4. I will
  then introduce a proposed anomaly detection pipeline\, highlighting vario
 us techniques for detecting anomalies in time series data\, including both
  statistical approaches and machine learning-based methods. By combining d
 omain expertise with data-driven methodologies\, this work aims to enhance
  the validation of Gaia’s photometric time series data.
LOCATION:Martin Ryle Seminar Room\, KICC
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