首页|Studies from University of Chile Provide New Data on Machine Learning (Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies)
Studies from University of Chile Provide New Data on Machine Learning (Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Data detailed on artificial intelligence have been presented. According to news reporting originating from Santiago, Chile, by NewsRx correspondents, research stated, “Stellar chemical abundances have proved themselves a key source of information for understanding the evolution of the Milky Way, and the scale of major stellar surveys such as GALAH have massively increased the amount of chemical data available.” Our news journalists obtained a quote from the research from University of Chile: “However, progress is hampered by the level of precision in chemical abundance data as well as the visualization methods for comparing the multidimensional outputs of chemical evolution models to stellar abundance data. Machine learning methods have greatly improved the former; while the application of tree-building or phylogenetic methods borrowed from biology are beginning to show promise with the latter. Here we analyse a sample of GALAH solar twins to address these issues. We apply The Cannon algorithm to generate a catalogue of about 40,000 solar twins with 14 high precision abundances which we use to perform a phylogenetic analysis on a selection of stars that have two different ranges of eccentricities.”
University of ChileSantiagoChileSouth AmericaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
