Investigating the faint radio population in the Lockman Hole with novel Machine Learning techniques

使用新颖的机器学习技术调查洛克曼洞中微弱的无线电人口

• 批准号: 2487070
• 金额: --
• 依托单位: University of Central Lancashire
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2487070
• 关键词: Investigating; faint; radio; population; Lockman

This project will utilise new machine learning techniques to decompose the relative contributions of Active Galactic Nuclei and Star Forming Galaxies to faint radio sources in the sub-mJy flux density range. This study will be carried out using joint e-MERLIN and VLA observations of the Lockman Hole. The result is a high resolution map of the target field, due to the long baselines of e-MERLIN, augmented by the excellent sensitivity of the VLA. Using these observations to morphologically identify these two sources in this flux range will provide evidence indicative of the global star formation rate history of the universe, as well as complimenting the results of similar work conducted in the GOODS-N field.

该项目将利用新的机器学习技术来分解活动星系核和星星形成星系对次mJy通量密度范围内微弱射电源的相对贡献。本研究将使用洛克曼洞的e-MERLIN和VLA联合观测进行。由于e-MERLIN的基线较长，加上VLA的出色灵敏度，结果是目标场的高分辨率地图。利用这些观测结果从形态学上确定这两个辐射源在这一通量范围内的位置，将提供表明宇宙全球星星形成速率历史的证据，并补充在GOODS-N领域进行的类似工作的结果。