Speaker(s) | Title and Abstract | Time | Material |
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Rafael Martinez Galarza | Harvesting outliers: data barriers to turn anomalies into discoveries
Over the last few years astronomers have become increasingly effective at identifying anomalous objects in large astronomical datasets. So far, that has meant "finding objects in sparsely populated regions of a multidimensional feature space". This is done using a number of methods that includes ensemble methods such as random forests searches, and more recently generative models that identify anomalies as those objects are more difficult to reconstruct by the trained model. This has produced huge lists of anomalies in diverse datasets that include SDSS galaxy spectra, Kepler and TESS light curves, and X-ray catalogs. Yet, most of those anomalies are not followed up, because of a cultural difficulty for scientists to interpret multi-dimensional scatter plots that have no labels in their axes. We argue that such cultural barrier can be overcome with novel ways to combine domain knowledge expertise with data visualization, or even incorporating domain knowledge directly into the anomaly detection algorithms. We would like to discuss ways in which VO tools can help in the identification of anomalies that represent true astronomical discoveries, by harvesting the currently publicly available catalogs of anomalies.
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7' | TBD |
Petr Skoda | SDSS redshift prediction based on Bayesian Deep Learning
|
7' | TBD |
Ashish Mahabal |
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7' | TBD |