首页|Studies from Liverpool John Moores University Reveal New Findings on Machine Lea rning (Applying machine learning to Galactic Archaeology: how well can we recove r the origin of stars in Milky Way-like galaxies?)
Studies from Liverpool John Moores University Reveal New Findings on Machine Lea rning (Applying machine learning to Galactic Archaeology: how well can we recove r the origin of stars in Milky Way-like galaxies?)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on artificial intelligen ce have been presented. According to news reporting from Liverpool, United Kingd om, by NewsRx journalists, research stated, "We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way -type galaxies from those that were formed externally and later accreted." Our news reporters obtained a quote from the research from Liverpool John Moores University: "These models, which include examples from artificial neural networ ks, decision trees and dimensionality reduction techniques, are trained on a sam ple of disc-like, Milky Way-mass galaxies drawn from the ARTEMIS cosmological hy drodynamical zoom-in simulations. We find that the input parameters which provid e an optimal performance for these models consist of a combination of stellar po sitions, kinematics, chemical abundances ([Fe/H] and [a/Fe]) and photometric properties. Mo dels from all categories perform similarly well, with area under the precision-r ecall curve (PR-AUC) scores of 0.6. Beyond a galactocentric radius of 5 kpc, mod els retrieve $>90 {{ \%}}$ of accreted stars, with a sample purity close to 60%, however the p urity can be increased by adjusting the classification threshold. For one model, we also include host galaxy-specific properties in the training, to account for the variability of accretion histories of the hosts, however this does not lead to an improvement in performance."
Liverpool John Moores UniversityLiverp oolUnited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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