HITS helps ESCAPE improve their data discoverability with machine learning

ESCAPE, the European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures, has successfully tested new machine learning prototypes that have improved search capabilities in some European Strategic Forum on Research Infrastructures (ESFRI) archives.

Among the ESCAPE partners who developed and tested the novel approach were also members of the Astroinformatics group (head: Kai Polsterer) at HITS. The other partners were  the Centre de Données astronomiques de Strasbourg (CDS) and the European Southern Observatory (ESO).

In a press release, ESCPAPE emphasizes that they adopted a novel AI explorative approach, rather than supervised learning methods, “to improve data discovery in broad variety of archive assets, considering the multitude of use-cases that scientists are involved in.  With millions of data files being made available to the science community, it is crucial for archives’ success to provide advanced query capabilities to guide the researchers in identifying data which are of interest to them.”

Read more here: ESCAPE press release