Developing optimal strategies for observing the electro-magnetic counterparts of gravitational-wave events

该项目的目标将是开发和应用贝叶斯方法来优化致密双星聚结引力波事件的电磁对应物的后续观测问题。这项工作的主要背景和重点将是在双中子星合并后对千年新星进行光学和近红外观测，但预计开发的贝叶斯工具将有更广泛的应用。该项目将寻求制定战略，以推断千年新星观测的最佳数量、历时、暴露时间和色带，以最大限度地增加发现千年新星的可能性和/或消除从其他瞬时事件中检测到的假阳性。所开发的方法将认识到并充分适应引力波干涉仪地面网络有限的天空定位能力，因此还将寻求研究以最佳方式对观测到引力波候选者的天空定位区域进行“平铺”的策略--建立在格拉斯哥最近制定的更现实的案例方法的基础上，并将其扩展到更现实的情况。这一系列研究可能涉及研究引力波源、千里新星和它们所在星系的性质之间的可能关系，并在格拉斯哥开展早期关于多信使概率函数的工作。开发的方法将涉及计算奇诺瓦探测与其他瞬时事件探测的贝叶斯后验赔率。随着项目的发展，将探索不仅简单地检测基诺瓦病毒，而且还表征和估计其来源参数的战略。机器学习方法可能适合于解决这些问题，并将为学生提供应用这些方法的培训。还将安排与LIGO观测站的长期长期连接，以确定与干涉仪相关的校准误差的特征--以及这些误差如何影响探测器网络的天空定位能力。

The objective of this project will be to develop and apply Bayesian methodology to the problem of optimising follow up observations of the electro-magnetic counterparts of compact binary coalescence gravitational-wave events. The main context and focus for this work will be optical and near infra-red observations of kilonovae, following binary neutron star coalescences, but it is anticipated that the Bayesian tools developed will have broader applications. The project will seek to develop strategies for inferring the optimal number, epoch, exposure time and colour bands of kilonovae observations that will maximise the probability of detecting the kilonova and/or eliminating false positive detections from other transient events. The methods developed will recognise and fully accommodate the limited sky localisation capabilities of ground-based networks of gravitational-wave interferometers, and will also therefore seek to investigate strategies for optimally 'tiling' the sky localisation regions in which the gravitational-wave candidates are observed - building on and extending to more realistic cases methodology recently developed at Glasgow. This strand of the research may involve investigation of possible relationships between gravitational-wave sources, kilonovae and their host galaxy properties, developing earlier work at Glasgow on the "multi-messenger probability function". The methods developed will involve computing Bayesian posterior odds ratios for kilonova detections versus detections of other transient events. As the project develops, strategies for not simply detecting kilonovae but also characterising and estimating their source parameters will be explored. Machine learning methods may be appropriate to address such problems, and training in the application of these methods will be provided to the student. An extended long-term attachment to the LIGO Observatories will also be arranged, to work on characterisation of the calibration errors associated with the interferometers - and how these affect the sky localisation capabilities of the detector network.