Given the ever-increasing size of astronomical data sets, even if our telescopes
During their last annual meeting,The team focused its efforts on objects whose brightness changes over time. Their system combines the strengths of machine learning algorithms and the indispensable knowledge of human experts to create a robust anomaly detection tool across billions of astronomical observations.
The group has also developed a specially designeda web interface to instantly visualize and compare each candidate with existing astronomical catalogs. This was done in order to facilitate the work of experts who need to compare the candidates for anomalies with any other publicly available information about the studied coordinates of the sky.
Quickly and easily separating artifacts from interesting anomaly candidates is critical for current and upcoming next-generation observatories.
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