There's more than one way to ride the wave: A Multi-Disciplinary Approach to Gravitational Wave Data Analysis
Lead Research Organisation:
CARDIFF UNIVERSITY
Department Name: School of Physics and Astronomy
Abstract
Amongst the strong gravitational wave detections, such as GW150914 (the first event), there will be a host of marginal signals. These weaker events will contain a wealth of information about the population of binary mergers in the Universe, and may even outnumber the strong signals.
Advanced data-processing and machine learning techniques will be deployed to better separate signals from the background noise and maximize the number of signals that can be extracted from the data. These additional signals will then be used to better understand the underlying population of black holes and neutron stars.
As the number of gravitational wave signals increases, machine learning and classification techniques will be used to understand the properties of the observed populations and uncover details of the formation and evolution of massive stars.
Advanced data-processing and machine learning techniques will be deployed to better separate signals from the background noise and maximize the number of signals that can be extracted from the data. These additional signals will then be used to better understand the underlying population of black holes and neutron stars.
As the number of gravitational wave signals increases, machine learning and classification techniques will be used to understand the properties of the observed populations and uncover details of the formation and evolution of massive stars.
Publications
Abbott B
(2019)
Low-latency Gravitational-wave Alerts for Multimessenger Astronomy during the Second Advanced LIGO and Virgo Observing Run
in The Astrophysical Journal
Abbott B
(2019)
Directional limits on persistent gravitational waves using data from Advanced LIGO's first two observing runs
in Physical Review D
Abbott B
(2019)
Search for Gravitational-wave Signals Associated with Gamma-Ray Bursts during the Second Observing Run of Advanced LIGO and Advanced Virgo
in The Astrophysical Journal
Abbott B
(2019)
GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
in Physical Review X
Abbott B
(2019)
Search for Gravitational Waves from a Long-lived Remnant of the Binary Neutron Star Merger GW170817
in The Astrophysical Journal
Abbott B
(2019)
Search for gravitational waves from Scorpius X-1 in the second Advanced LIGO observing run with an improved hidden Markov model
in Physical Review D
Abbott B
(2019)
Searches for Gravitational Waves from Known Pulsars at Two Harmonics in 2015-2017 LIGO Data
in The Astrophysical Journal
Abbott B
(2019)
Tests of General Relativity with GW170817
in Physical Review Letters
Abbott B
(2019)
Search for intermediate mass black hole binaries in the first and second observing runs of the Advanced LIGO and Virgo network
in Physical Review D
Abbott B
(2018)
Search for Subsolar-Mass Ultracompact Binaries in Advanced LIGO's First Observing Run
in Physical Review Letters
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/P006779/1 | 30/09/2017 | 29/09/2024 | |||
1945971 | Studentship | ST/P006779/1 | 30/09/2017 | 29/09/2021 | Rhys Green |
Description | OzGrav Gravitational Wave Parameter estimation face to face meeting |
Amount | $1,500 (AUD) |
Organisation | Australian Research Council |
Department | Centre of Excellence for Gravitational Wave Discovery |
Sector | Public |
Country | Australia |
Start | 02/2019 |
End | 03/2019 |
Description | LIGO Scientific Collaboration |
Organisation | LIGO Scientific Collaboration |
Country | United States |
Sector | Academic/University |
PI Contribution | Regularly carrying out analysis for the collaboration and contributing to LSC publications |
Collaborator Contribution | Regular training, discussions and meetings |
Impact | Detection of Gravitational Waves, multiple published papers in academic journals |
Description | Oracle Internship |
Organisation | Oracle Corporation |
Country | United States |
Sector | Private |
PI Contribution | I contributed Code to the Oracle AI apps team over a 6 month placement |
Collaborator Contribution | Training, regular meetings and transfer of knowledge |
Impact | Development of Oracle AI Apps software |
Start Year | 2017 |