Another project demonstrates how the data reduction of a meteor surveillance network known as CAMS (Cameras for Allsky Meteor Surveillance) could be automated to identify new meteor shower clusters—potentially the trails of ancient Earth crossing Comets. Since the AI pipeline has been put into place a total of nine new meteor showers have been discovered via CAMS.
“SpaceML helped accelerate impact by bringing in a team of citizen scientists who deployed an interpretable Active Learning and AI-powered meteor classifier to automate insights, allowing the astronomers focused research for the SETI CAMS project,” said Siddha Ganju, Self Driving and Medical Instruments AI Architect, Nvidia (founding member of SpaceML’s CAMS and Worldview Search Initiatives). “During SpaceML we (1) standardized the processing pipeline to process the decade long meteor dataset collected by CAMS, and, established the state of the art meteor classifier with a unique augmentation strategy; (2) enabled active learning in the CAMS pipeline to automate insights; and, (3) updated the NASA CAMS Meteor Shower Portal which now includes celestial reference points and a scientific communication tool. And the best thing is that future citizen scientists can partake in the CAMS project by building on the publicly accessible trained models, scripts, and web tools.”
SpaceML also hosts INARA (Intelligent ExoplaNET Atmospheric RetrievAI), a pipeline for atmospheric retrieval based on a synthesized dataset of three million planetary spectra, to detect evidence of possible biological activity in exoplanet atmospheres—in other words, “Are We Alone?”
SpaceML.org seeks to curate a central repository of project notebooks and datasets generated from projects similar to those listed above. These project repositories contain a Google “Co-Lab’ notebook that walks users through the dataset and includes a small data snippet for a quick test drive before committing to the entire data set (which are invariably very large).
The projects also house the complete dataset used for the challenges, which can be made available upon request. Additionally, SpaceML seeks to facilitate the management of new datasets that result from ongoing research and in due course run tournaments to invite improvements on ML models (and data) against known benchmarks.
“We were concerned on how to make our AI research more reproducible,” said James Parr, FDL Director and CEO, Trillium Technologies. “We realized that the best way to do this was to make the data easily accessible, but also that we needed to simplify both the on-boarding process, initial experimentation and workflow adaptation process.”
“The problem with AI reproducibility isn’t necessarily, ‘not invented here’ – it’s more, ‘not enough time to even try.” We figured if we could share analysis ready data, enable rapid server-side experimentation and good version control, it would be the best thing to help make these tools get picked up by the community for the benefit of all.”
FDL launches its 2021 program on June 16, 2021, with researchers in the US addressing seven challenges in the areas of Heliophysics, Astronaut Health, Planetary Science and Earth Science. The program will culminate in mid-August, with teams showcasing their work in a virtual event.
Visit SpaceML.org: www.SpaceML.org
SpaceML.org: A new resource to accelerate AI application in space science and exploration (2021, June 18)
retrieved 18 June 2021
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