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Using Machine Learning to Find Gravitationally-Lensed Quasars and Supernovae

Using Machine Learning to Find Gravitationally-Lensed Quasars and Supernovae
使用机器学习寻找引力透镜类星体和超新星
批准号:
2132300
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
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英文摘要
The Large Synoptic Survey Telescope (LSST) will play a main role in revolutionising strong gravitational lensing in the 2020s. LSST will be able to detect many tens of thousands of new strong gravitational lens events (e.g. Collett 2015), thanks to its expected image quality (0.6'' FWHM including telescope, atmosphere and wind). These lenses will be complementary to those detected by Euclid and other surveys, with LSST probing a much fainter source population and benefitting both from multicolour data and a cadence well-suited to lensed quasar and SNe discovery.
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Understanding structural evolution of galaxies with machine learning
  • 批准号:
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2022
  • 负责人:
    Nicola Rosario Napolitano
  • 依托单位: