📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

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

Lead Research Organisation: University of Glasgow
Department Name: School of Physics and Astronomy

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/R504750/1 30/09/2017 29/09/2021
2039699 Studentship ST/R504750/1 30/09/2017 31/12/2021 Laurence Datrier
ST/P006809/1 30/09/2017 29/09/2024
2039699 Studentship ST/P006809/1 30/09/2017 31/12/2021 Laurence Datrier
NE/W503058/1 31/03/2021 30/03/2022
2039699 Studentship NE/W503058/1 30/09/2017 31/12/2021 Laurence Datrier