Next-Generation Radio Surveys for Cosmic Magnetism Science

在这个项目中，学生将研究MeerKAT MIGHTEE调查的光谱数据，特别强调偏振成分，以产生一个带有定量估计偏差的分类对象目录。与MIGHTEE团队合作，该学生将开发MeerKAT调查数据的偏振校准和成像处理，以使用机器学习方法生成分类对象目录，并对偏差进行定量估计。该项目后期的一个重点将是理解机器学习分类训练数据中选择偏差的影响，以及这些偏差对天体物理解释和参数估计的影响。

In this project the student will work on spectral data from the MeerKAT MIGHTEE survey, with particular emphasis on the polarisation components, to produce a classified object catalogue with quantitative estimates of bias. In collaboration with the MIGHTEE team, the student will develop the polarisation calibration and imaging processing for MeerKAT survey data to produce a classified object catalogue with quantitative estimates of bias using machine learning methodologies. A key focus within the later part of this project will be understanding the effects of selection bias in the training data for the machine learning classification and the impact of these biases on astrophysical interpretation and parameter estimation.