首页|Research on Machine Learning Reported by Researchers at University of Nottingham (A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z 8)
Research on Machine Learning Reported by Researchers at University of Nottingham (A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z 8)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on artificial intelligence. According to news reporting out of Nottingham, United Kingdom, by NewsRx editors, research stated, "Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively investigated at low redshift, it is crucial to develop a robust system for characterizing galaxy morphologies at earlier cosmic epochs." Financial supporters for this research include Nasa. Our news correspondents obtained a quote from the research from University of Nottingham: "Relying solely on nomenclature established for low-redshift galaxies risks introducing biases that hinder our understanding of this new regime. In this paper, we employ variational autoencoders to perform feature extraction on galaxies at z >2 using JWST/NIRCam data. Our sample comprises 6869 galaxies at z >2, including 255 galaxies at z >5, which have been detected in both the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey Hubble Space Telescope fields and the Cosmic Evolution Early Release Science Survey done with JWST, ensuring reliable measurements of redshift, mass, and star formation rates. To address potential biases, we eliminate galaxy orientation and background sources prior to encoding the galaxy features, thereby constructing a physically meaningful feature space. We identify 11 distinct morphological classes that exhibit clear separation in various structural parameters, such as the concentration, asymmetry, and smoothness (CAS) metric and M _20 , Sersic indices, specific star formation rates, and axis ratios. We observe a decline in the presence of spheroidal-type galaxies with increasing redshift, indicating the dominance of disk-like galaxies in the early Universe."
University of NottinghamNottinghamUnited KingdomEuropeCyborgsEmerging TechnologiesMachine Learning
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