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
(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)
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 short gravitational-wave bursts in the third Advanced LIGO and Advanced Virgo run
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)
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
(2021)
Population Properties of Compact Objects from the Second LIGO-Virgo Gravitational-Wave Transient Catalog
in The Astrophysical Journal Letters
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)
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
(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)
All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems
in Physical Review D
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/T506102/1 | 30/09/2019 | 29/09/2023 | |||
2285031 | Studentship | ST/T506102/1 | 30/09/2019 | 30/03/2023 | Michael Williams |