I recently gave a summary talk at the EIROForum workshop on Big Data showing fresh from the oven results from our Deep learning project to identify asteroid trails in Hubble Space Telescope images from the European HST archive by using Google’s AutoML Vision API. This work has been done mostly in collaboration with my colleague Sandor Kruk, a Research Fellow at ESTEC, and Pablo García Martín, an engineer lead at Safran currently doing a Ph.D. in Astronomy with us applying these machine learning algorithms to data at the ESAC Science Data Centre plus other colleagues.
In short, we have managed to train an AutoML classifier by feeding training samples of between 1000 and 2000 images with asteroid trails as manually selected from our Zooniverse project Hubble Asteroid Hunter, where more than 10,000 volunteers have found over 1200 asteroid trails in all the HST ACS/WFC and WFC3/UVIS images at the ESA Hubble Science Archive. We have used both the on-cloud trainer and the edge trainer, and had to split the large images in four quadrants to maximise the ability of the network to identify trails given their relative sizes compared to the those of the images. The result has been a staggering 2,466 asteroid trails identified in 2,200 images (approximately 5% of all the images inspected), out of which 1,222 asteroid trails had already been identified by the Zooniverse volunteers while another 1,244 are new asteroid trails found. We have used 3 elapsed hours (122 computer node hours) for training the network and 7 elapsed hours (38 computer node hours) to scan the whole archive.
You can watch a full video of the presentation here:
And download the presentation in pdf here.
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