首页|New Machine Learning Findings from University of Virginia Published (Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine-learning Techniques)
New Machine Learning Findings from University of Virginia Published (Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine-learning Techniques)
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Research findings on artificial intelligence are discussed in a new report. According to news reporting from Charlottesville, Virginia, by NewsRx journalists, research stated, "Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties." Financial supporters for this research include John F. Angle Fellowship; Research Corporation For Science Advancement; David And Lucile Packard Foundation; Nasa. Our news journalists obtained a quote from the research from University of Virginia: "Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically, we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic-ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO abundance compared to any singular physical parameter. Moreover, in general we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic-ray environment and in disks with higher initial C/O ratios."
University of VirginiaCharlottesvilleVirginiaUnited StatesNorth and Central AmericaChemistryCyborgsEmerging TechnologiesMachine Learning
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