Intermediate and Low-mass Young Stellar Populations in the Magellanic Clouds

麦哲伦星云中的中质量和低质量年轻恒星族

• 批准号: 2758988
• 金额: --
• 依托单位: Keele University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2758988
• 关键词: Intermediate; Low; mass; Young; Stellar

The Large and Small Magellanic Clouds (MCs) are the nearest templates for the detailed study of star formation under metal-poor conditions. These galaxies mirror the conditions typical of galaxies during the early phases of their assembly, providing a stepping stone to understand star formation at high redshift where such processes cannot be directly observed. This PhD research will use near-IR photometry from the Vista Magellanic Cloud (VMC) Survey combined with optical photometry from the SMASH survey to constrains and characterise young stellar populations in the MCs. This project focuses on two 1.2 degree squared regions in the sky, one in each galaxy, that contain well over two million sources observed over 20 epochs. The student will perform PSF photometry on the VMC images of these regions, using tools developed by consortium members, for which at medium length work visit is envisaged. The point sources represent different stellar populations, that need to be disentangled so that the young stars can be identified and characterised. The student will use statistical methods including supervised machine learning to identify young stars, construct the Initial Mass Function across whole star formation complexes, map and quantify the true extent of these regions and the interplay with the gas and dust. This project can also make use of the VMC survey multi-epoch data to characterise massive young stellar variability across the MCs, to constrain any environmental dependence on the galactic environment. The available light curves need to be analysed to identify and characterise variables, the most extreme of which are the young stars.

大小的麦哲伦云（MCS）是在金属贫困条件下详细研究恒星形成的最接近模板。这些星系反映了它们组装的早期阶段期间星系的典型条件，提供了一个垫脚石，可以在高红移处理解恒星形成，在该过程中无法直接观察到此类过程。这项博士学位研究将使用Vista Magellanic Cloud（VMC）调查的近IR光度法结合SMASH调查中的光学测定法来限制和表征MC中的年轻恒星种群。该项目的重点是天空中的两个1.2度平方区域，每个星系中有一个，其中包含超过20个以上时代的来源。该学生将使用财团成员开发的工具对这些区域的VMC图像进行PSF光度法，并设想中等长度的工作访问。点源代表了不同的恒星种群，需要分解，以便可以识别和表征年轻的恒星。学生将使用包括监督机器学习在内的统计方法来识别年轻的恒星，在整个恒星形成络合物中构建初始质量功能，绘制并量化这些区域的真实程度以及与气体和尘埃的相互作用。该项目还可以利用VMC调查多上述数据来表征MCS的巨大年轻恒星变异性，从而限制对银河系环境的任何环境依赖性。需要分析可用的光曲线以识别和表征变量，其中最极端的是年轻的恒星。