Accelerating gravitational wave astrophysics with machine learning
Lead Research Organisation:
University of Glasgow
Department Name: College of Science and Engineering
Abstract
The recent discovery of gravitational waves from the coalescence of black holes and neutron stars has opened a new window on the universe, greatly advancing our understanding of compact binaries. As the LIGO and Virgo detectors continue to improve, they will see further into the universe, greatly increasing the rate of detections. As the noise level drops, the loudest signals will also stand out against the noisy data, reducing the statistical uncertainties in our analysis of their properties. At the same time, the new field of multi-messenger astronomy is demanding that we analyse the data quicker, to drive down the latency with which we pass on information for follow-up.
Meeting these challenges and delivering the best possible science from gravitational wave detections requires us to deliver results faster, from more complex models that reduce systematic uncertainties. An answer to these challenges may be found by making use of the recent major advances in machine learning through the use of neural networks. In particular, the aim of this project is to accelerate gravitational-wave inference using the methods of variational inference and neural-network enhances sampling algorithms. This in turn will also allow us to use more sophisticated models to avoid systematic errors, and ultimately deliver the most detailed and robust possible results from the ground-based network of gravitational wave observatories. This addresses several of STFC's strategic priorities, particularly in ensuring our continued leadership in gravitational wave astronomy, making use of the STFC-funded Advanced LIGO and A+ facilities. The methodology we will be using is also highly relevant for developing skills in artificial intelligence and data-intensive science that are aligned with STFC's goal of maintaining UK leadership in this area.
Meeting these challenges and delivering the best possible science from gravitational wave detections requires us to deliver results faster, from more complex models that reduce systematic uncertainties. An answer to these challenges may be found by making use of the recent major advances in machine learning through the use of neural networks. In particular, the aim of this project is to accelerate gravitational-wave inference using the methods of variational inference and neural-network enhances sampling algorithms. This in turn will also allow us to use more sophisticated models to avoid systematic errors, and ultimately deliver the most detailed and robust possible results from the ground-based network of gravitational wave observatories. This addresses several of STFC's strategic priorities, particularly in ensuring our continued leadership in gravitational wave astronomy, making use of the STFC-funded Advanced LIGO and A+ facilities. The methodology we will be using is also highly relevant for developing skills in artificial intelligence and data-intensive science that are aligned with STFC's goal of maintaining UK leadership in this area.
Organisations
People |
ORCID iD |
John Veitch (Primary Supervisor) | |
Michael Williams (Student) |
Publications
Abbott R
(2021)
All-sky search for continuous gravitational waves from isolated neutron stars in the early O3 LIGO data
in Physical Review D
Abbott R
(2022)
All-sky search for gravitational wave emission from scalar boson clouds around spinning black holes in LIGO O3 data
in Physical Review D
Abbott R
(2021)
All-sky search for long-duration gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
in Physical Review D
Abbott R
(2021)
All-sky search for short gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
in Physical Review D
Abbott R
(2021)
All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems
in Physical Review D
Abbott R
(2022)
All-sky, all-frequency directional search for persistent gravitational waves from Advanced LIGO's and Advanced Virgo's first three observing runs
in Physical Review D
Abbott R
(2021)
Constraints from LIGO O3 Data on Gravitational-wave Emission Due to R-modes in the Glitching Pulsar PSR J0537-6910
in The Astrophysical Journal
Abbott R
(2021)
Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run.
in Physical review letters
Abbott R
(2022)
Constraints on dark photon dark matter using data from LIGO's and Virgo's third observing run
in Physical Review D
McGinn J
(2021)
Generalised gravitational wave burst generation with generative adversarial networks
in Classical and Quantum Gravity
Abbott R
(2020)
Gravitational-wave Constraints on the Equatorial Ellipticity of Millisecond Pulsars
in The Astrophysical Journal Letters
Abbott R
(2021)
GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo during the First Half of the Third Observing Run
in Physical Review X
Williams M
(2023)
Importance nested sampling with normalising flows
Williams M
(2023)
Importance nested sampling with normalising flows
in Machine Learning: Science and Technology
Abbott R
(2022)
Narrowband Searches for Continuous and Long-duration Transient Gravitational Waves from Known Pulsars in the LIGO-Virgo Third Observing Run
in The Astrophysical Journal
Williams M
(2021)
Nested Sampling with Normalising Flows for Gravitational-Wave Inference
Williams M
(2021)
Nested sampling with normalizing flows for gravitational-wave inference
in Physical Review D
Abbott R
(2021)
Observation of Gravitational Waves from Two Neutron Star-Black Hole Coalescences
in The Astrophysical Journal Letters
Abbott R
(2021)
Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog
in The Astrophysical Journal Letters
Saha S
(2024)
Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder
in The Astrophysical Journal
Abbott R
(2021)
Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo's first three observing runs
in Physical Review D
Abbott R
(2022)
Search for continuous gravitational wave emission from the Milky Way center in O3 LIGO-Virgo data
in Physical Review D
Abbott R
(2022)
Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data
in Physical Review D
Abbott R
(2022)
Search for intermediate-mass black hole binaries in the third observing run of Advanced LIGO and Advanced Virgo
in Astronomy & Astrophysics
Abbott R
(2021)
Search for Lensing Signatures in the Gravitational-Wave Observations from the First Half of LIGO-Virgo's Third Observing Run
in The Astrophysical Journal
Abbott R
(2022)
Search for Subsolar-Mass Binaries in the First Half of Advanced LIGO's and Advanced Virgo's Third Observing Run.
in Physical review letters
Abbott R
(2022)
Search of the early O3 LIGO data for continuous gravitational waves from the Cassiopeia A and Vela Jr. supernova remnants
in Physical Review D
Abbott R
(2021)
Searches for Continuous Gravitational Waves from Young Supernova Remnants in the Early Third Observing Run of Advanced LIGO and Virgo
in The Astrophysical Journal
Abbott R
(2021)
Tests of general relativity with binary black holes from the second LIGO-Virgo gravitational-wave transient catalog
in Physical Review D
Hayes F
(2023)
Unpacking Merger Jets: A Bayesian Analysis of GW170817, GW190425 and Electromagnetic Observations of Short Gamma-Ray Bursts
in The Astrophysical Journal
Abbott R
(2021)
Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo's third observing run
in Physical Review D
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/T506102/1 | 01/10/2019 | 30/09/2023 | |||
2285031 | Studentship | ST/T506102/1 | 01/10/2019 | 31/03/2023 | Michael Williams |