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Supermassive black holes through cosmic time: Exploiting the era of Big Data in astronomy

Supermassive black holes through cosmic time: Exploiting the era of Big Data in astronomy
穿越宇宙时间的超大质量黑洞:利用天文学大数据时代
批准号:
2488912
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:
2020
资助国家:
英国
项目状态:
已结题
起止时间:
2020 至 --

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中文摘要
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英文摘要
How do supermassive black holes form? How are they influencing and are influenced by their host galaxies? The 2020s will see a see change in how we tackle the big questions in astronomy. With the advent of the Vera Rubin Observatory's Legacy Survey of Space and Time (LSST), the entire sky will be observed within 2-3 days in several optical bands to unprecedented depth over a total span of 10 years. We will be able to study the evolution of galaxies with samples containing billions of objects. At the same time, the repeat observations will provide us with the dynamic picture of the growth of supermassive black holes in the centre of these galaxies. In Southampton, we are leading several LSST-related research activities on galaxy evolution and active galactic nuclei (AGN). The key to solving these questions with the vast amount of data becoming available soon is knowledge of techniques to handle, analyse, and interpret them - the "Big Data Challenge in Astronomy." Dr Sebastian Hoenig and Dr Manda Banerji are offering a PhD studentship as part of the Centre of Doctoral Training (CDT) DISCnet. The student will be working on multi-wavelength surveys, including the Southampton-led VISTA Extragalactic Infrared Legacy Survey VEILS, and will prepare and exploit LSST and supporting survey (including 4MOST) data. This will range from compiling and analysing LSST pre-cursor data to leading spectroscopic follow-up observations of targets of interest. The student will benefit from the A-graded training and research environment, including residential courses in Machine Learning, Statistics, Big Data, and Parallel programming on High-Performance Computers.
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空间分数阶 Black-Scholes 方程的波动率反演 问题
  • 批准号:
    Q24A010012
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2024
  • 负责人:
    蒋晓颖
  • 依托单位:
Black-Scholes期权定价模型的时间自适应算法与分析
  • 批准号:
    12271142
  • 项目类别:
    面上项目
  • 资助金额:
    45万元
  • 批准年份:
    2022
  • 负责人:
    任金城
  • 依托单位:
投资者非理性认知环境下的股票收益预测与投资组合研究
  • 批准号:
    72061002
  • 项目类别:
    地区科学基金项目
  • 资助金额:
    28.0万元
  • 批准年份:
    2020
  • 负责人:
    谢军
  • 依托单位:
Shining light on the black hole mass distribution
  • 批准号:
    12073029
  • 项目类别:
    面上项目
  • 资助金额:
    61.0万元
  • 批准年份:
    2020
  • 负责人:
    Roberto Soria
  • 依托单位: