Improved methods for the detection of gravitational waves associated with gamma-ray bursts
Lead Research Organisation:
Cardiff University
Department Name: School of Physics and Astronomy
Abstract
The long-anticipated birth of gravitational-wave astronomy will
occur in the next few years with the advent of the Advanced
LIGO and Advanced Virgo gravitational-wave detectors. These
instruments will open a new channel for studying the most extreme phenomena and
environments found in nature, including gamma-ray bursts, core-collapse
supernovae, and black-hole mergers. The inner engines of these systems are either
obscured or inherently invisible to electromagnetic observations. Furthermore, the
associated gravitational-wave emission typically depends on poorly understood
physics, such as the equation-of-state of matter at supra-nuclear densities.
Gravitational waves will therefore provide an exciting new probe of these
astrophysical systems, for example constraining the neutron star equation-of-state,
and providing laboratories for tests of fundamental physics and cosmology. However,
realising the potential of gravitational waves poses a significant challenge: state-of the-
art techniques for detecting and interpreting gravitational waves require precise
theoretical models of the gravitational-wave emission, and hence are not applicable
to most gravitational-wave sources. This project aims at maximising the scientific
exploitation of gravitational waves through advancements beyond current state-of the-
art in rapid automated analyses, advanced signal/background discrimination, and
waveform reconstruction. The goals of this project are: (i) to develop the model independent
techniques needed to robustly detect gravitational waves from
relativistic transient events, and determine the signal structure; (ii) to apply these to
data from the Advanced LIGO / Advanced Virgo network to detect GWs; and (
occur in the next few years with the advent of the Advanced
LIGO and Advanced Virgo gravitational-wave detectors. These
instruments will open a new channel for studying the most extreme phenomena and
environments found in nature, including gamma-ray bursts, core-collapse
supernovae, and black-hole mergers. The inner engines of these systems are either
obscured or inherently invisible to electromagnetic observations. Furthermore, the
associated gravitational-wave emission typically depends on poorly understood
physics, such as the equation-of-state of matter at supra-nuclear densities.
Gravitational waves will therefore provide an exciting new probe of these
astrophysical systems, for example constraining the neutron star equation-of-state,
and providing laboratories for tests of fundamental physics and cosmology. However,
realising the potential of gravitational waves poses a significant challenge: state-of the-
art techniques for detecting and interpreting gravitational waves require precise
theoretical models of the gravitational-wave emission, and hence are not applicable
to most gravitational-wave sources. This project aims at maximising the scientific
exploitation of gravitational waves through advancements beyond current state-of the-
art in rapid automated analyses, advanced signal/background discrimination, and
waveform reconstruction. The goals of this project are: (i) to develop the model independent
techniques needed to robustly detect gravitational waves from
relativistic transient events, and determine the signal structure; (ii) to apply these to
data from the Advanced LIGO / Advanced Virgo network to detect GWs; and (
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/N504002/1 | 01/10/2015 | 31/03/2021 | |||
| 1644622 | Studentship | ST/N504002/1 | 01/10/2015 | 31/03/2019 | Iain Dorrington |
| Description | I was tasked with improving data analysis techniques for the LIGO scientific collaboration, which searches for gravitational waves (GWs). I had two projects to try and achieve this. One was to use machine learning methods to make a more sensitive search for waveforms of unknown morphology. The second was to rewrite an existing data analysis pipeline, PyGRB that searches for GWs associated with gamma ray bursts (GRBs), to take advantage of new data analysis tools that have been developed and to make it run faster. Both of these tasks have been achieved or are close to being achieved. The machine learning pipeline is approximately 10% more sensitive, though I am still running this pipeline more to definitively prove the improvement. The improvements to PyGRB are not quite finished, though we expect it to be completed soon, and to run much faster. |
| Exploitation Route | Other people will run the pipelines I have developed to detect GWs, and work on further improvements. |
| Sectors | Other |
| Description | Using the data analysis and coding techniques I learned as part of my PhD, I took two weeks out from my PhD work to help a charity (the south riverside community development centre) analyse poverty and deprivation data in the local area to help the charity better target it's services and to give them data to use when applying for funding. |
| First Year Of Impact | 2018 |
| Sector | Other |
| Impact Types | Societal |
| Title | Coherent Matched Filtering for PyCBC |
| Description | Gravitational wave astronomy uses a global network of Gravitational wave detectors.There are a few different ways we can combine the data from each of these detectors. Each detector is sensitive to different parts of the sky, and different Gravitational wave frequencies. If the detectors network finds a signal, we can check whether it is consistent with what we know about the sensitivity of the detectors to the signals frequency and sky location. In this way we can make a new detection statistic that is better at rejecting noise. |
| Type Of Technology | Software |
| Year Produced | 2018 |
| Impact | This new data analysis technique is already able to run on real data. It has been tested on data that was known to contain a signal and was able to detect it. The gravitational wave detectors are currently offline until the end of the year. By the time they come back on it is planned that we will be able to use coherent matched filtering. |
| URL | https://doi.org/10.5281/zenodo.1183449 |
| Title | Machine learning pipeline for Gravitational wave astronomy |
| Description | A machine learning data analysis pipeline to look for gravitational waves associated with gamma ray bursts. |
| Type Of Technology | Software |
| Year Produced | 2015 |
| Impact | Improved sensitivity to GWs. |