Radio Transients and Machine Learning

亚历克斯·安德森将致力于一个项目，寻找、发现、分类和调查来自猫鼬射电望远镜的射电数据中的共生场瞬变。在这个过程中，他将开发机器学习技术，并通过Zooniverse与公民科学家合作，开发将人类和机器分类相结合的技术。这项工作的动机是在我们的数据中发现了大量新的射电变量和瞬变，以及开发新工具的需要，以最大化他们研究的科学回报。Meerkat的数据分析和为这个和未来的射电望远镜开发的新工具与英国对平方公里阵列的关键战略投资非常一致，Meerkat是该阵列的两个官方先驱之一。将开发新的工具，影响可能非常大。

Alex Andersson will work on a project to look find, classify and investigate commensal field transients in radio data from the MeerKAT radio telescope. In the process he will develop machine learning techniques and work with citizen scientists via Zooniverse, developing techniques for combining human and machine classifications. The work is motivated by the flood of new radio variables and transients being discovered in our data, and the need to develop new tools to maximise the scientific reward from their study. The MeerKAT data analysis and development of new tools for this and future radio telescopes aligns well with the UK's key strategic investment in the Square Kilometre Array, for which MeerKAT is one of two official precursors. Novel tools will be developed, and impact is potentially very high.