Hi, I am Bruno Merín, the head of the ESAC Science Data Centre, at ESAC, near Madrid, in Spain. At the ESAC Science Data Centre we host the data for ESA Science Missions (both Astronomy, Planetary, Heliophysics and even from the Human and Robotic Exploration directorate).
I am interested in the preservation, dissemination and exploitation of the data from Space Science missions, also in scientific software, archival data-driven research in general, usability of user interfaces and citizen science projects. I am passionate about bringing high quality Space Science data (data from the final frontiers) to the people on Earth.
Scientifically, I am interested in young stars and exoplanets, mostly in connecting young transitional disks with exoplanets around young stars and with the broad emerging statistics of currently known exoplanet populations around main sequence stars.
Using deep learning to identify asteroid trails on ESA's Hubble data archive
The ESAC Science Data Centre is conducting Machine Learning experiments to provide AI-supported added value services to our users in the mid-term future. This presentation will describe some of the experiments conducted and our analysis of the applicability of Machine Learning in different steps of our value chain (data production, curation, archiving, valorisation, dissemination and support to users in their exploitation).
In particular, in the area of data valorisation, I will present results from one such projects, were we have used the Google AutoML Vision API to identify asteroid trails on archival HST images. For this project, we have used labels of asteroid trails from volunteer markings from the Zooniverse asteroidhunter.org project to train a Deep Learning Convolutional Neural Network embedded into Google's AutoML Vision API and then have used it to scan the whole archive to look for new asteroid trails that the volunteers had not identified. I will describe how the Precision and Recall of the machine learning model depend on the pre-processing of the images and show the very promising results from this analysis. I will end up by elaborating on how these type of initiatives will help enriching the search for data-driven patterns on archival data in the mid-term future.