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
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Patrick Sutton (Primary Supervisor) | |
| Iain Dorrington (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/N504002/1 | 30/09/2015 | 30/03/2021 | |||
| 1644622 | Studentship | ST/N504002/1 | 30/09/2015 | 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. |