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
Soares-Santos M
(2019)
First Measurement of the Hubble Constant from a Dark Standard Siren using the Dark Energy Survey Galaxies and the LIGO/Virgo Binary-Black-hole Merger GW170814
in The Astrophysical Journal
Lorenzano S
(2019)
Early molecular oxidative stress biomarkers of ischemic penumbra in acute stroke.
in Neurology
Lorenzano S
(2018)
Oxidative Stress Biomarkers of Brain Damage: Hyperacute Plasma F2-Isoprostane Predicts Infarct Growth in Stroke.
in Stroke
Burns E
(2019)
A Fermi Gamma-Ray Burst Monitor Search for Electromagnetic Signals Coincident with Gravitational-wave Candidates in Advanced LIGO's First Observing Run
in The Astrophysical Journal
Albert A
(2019)
Search for Multimessenger Sources of Gravitational Waves and High-energy Neutrinos with Advanced LIGO during Its First Observing Run, ANTARES, and IceCube
in The Astrophysical Journal
Abbott BP
(2019)
Constraining the p-Mode-g-Mode Tidal Instability with GW170817.
in Physical review letters
Abbott BP
(2018)
Search for Subsolar-Mass Ultracompact Binaries in Advanced LIGO's First Observing Run.
in Physical review letters
Abbott BP
(2018)
GW170817: Measurements of Neutron Star Radii and Equation of State.
in Physical review letters
Abbott BP
(2019)
Tests of General Relativity with GW170817.
in Physical review letters
Abbott BP
(2019)
Search for Subsolar Mass Ultracompact Binaries in Advanced LIGO's Second Observing Run.
in Physical review letters
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/P006779/1 | 01/10/2017 | 30/09/2024 | |||
| 1945971 | Studentship | ST/P006779/1 | 01/10/2017 | 30/09/2021 | 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 |