VHE Gamma-ray astronomy with the Cherenkov Telescope Array

学生将有助于CTA设计，施工和早期科学的三个方面。该项目的目的和目标是双重的，既有技术方面的，也有天文方面的。在技术方面，学生将研究切伦科夫望远镜图像中空气簇射分类的新方法（即，无论它们起源于轻子还是强子）。该项目的科学方面旨在利用从无线电到伽马射线的宽带多频方法，调查银河系中所谓的“费米泡”中的相对论粒子群。该项目属于STFC科学领域：D：1。当物理定律被推向极端时，它是如何发挥作用的？D：2。高能粒子和引力波如何告诉我们极端宇宙的情况？D：3。超紧凑物体是如何形成的，它们的性质是什么，极端重力如何影响它们的周围环境？合作者包括利物浦、莱斯特和达勒姆大学以及海德堡MPIK。据设想，学生将在MPIK有一个长期的附件。该项目的机器学习方面可能会对火焰化学等领域的超高速图像分类应用产生影响。

The student will contribute to three aspects of CTA design, construction and early science. The aims and objectives of the project are two-fold, having both a technical and an astronomical component. On the technical side student will investigate novel methods for the classification of air showers in Cherenkov telescope images (i.e., whether they are leptonic or hadronic in origin) using machine learning techniques. The scientific side of the project is aimed at investigating the populations of relativistic particles in the so-called "Fermi bubbles" in the Milky Way using a broad-band multi-frequency approach from radio to gamma-rays. This project lies in the STFC science areas:D:1. How do the laws of physics work when driven to the extremes?D:2. How can high energy particles and gravitational waves tell us about the extreme universe?D:3. How do ultra-compact objects form, what is their nature and how does extreme gravity impact on their surroundings?Collaborators include the Universities of Liverpool, Leicester and Durham and MPIK Heidelberg. It is envisaged that the student will have a long-term attachment at MPIK. The machine learning aspects of the project may have impact in application to very-high-speed image classification in areas such as e.g. flame chemistry.