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Exploring the Gravitational-Wave Sky with Machine Learning


Cardiff School of Physics and Astronomy

Cardiff United Kingdom Astrophysics Data Analysis

About the Project

Gravitational waves (GWs) are produced by some of the most violent events in the Universe, such as the mergers of black holes and the explosive deaths of massive stars. Rapid detection of such signals can trigger follow-up observations by other facilities including ground-based telescopes, satellites, and neutrino observatories, greatly increasing the scientific payoff of such discoveries.

For example, combined observations of the binary neutron star merger GW170817 revealed the origin of heavy elements in the Universe and provided a new way to measure the Hubble constant.

Detecting such GW events before the electromagnetic counterpart fades requires analysis of the gravitational-wave data on timescales of minutes or less. Recently, machine learning techniques based on Convolutional Neural Networks (CNNs) have been demonstrated to have the potential for sub-second-latency analysis of data from GW detector networks. The aim of this project is to develop and implement a CNN with the ability to detect generic transient signals, such as those expected from newly formed or perturbed black holes and neutron stars. The analysis will be run in real time during upcoming observing runs by the LIGO-Virgo-KAGRA detector network, to detect, characterise, and determine the location on the sky of GW signals. We will issue public alerts about detected events, with special emphasis on signals associated with gamma-ray bursts, core-collapse supernovae, and other relativistic astrophysical phenomena.

This project involves developing expertise in programming, signal processing, high-throughput computing, and high-performance computing. You will collaborate with other GW observers, GW theorists, and astronomers in the Cardiff University Gravity Exploration Institute and in the LIGO Scientific Collaboration. The project may optionally involve a long-term (up to four months) secondment to one of the LIGO observatories in the US.

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society.

Eligibility 

The typical academic requirement is a minimum of a 2:1 undergraduate degree in biological and health sciences; mathematics and computer science; physics and astronomy or a relevant discipline.  Candidates should be interested in AI and big data challenges, and in (at least) one of the three research themes. You should have an aptitude and ability in computational thinking and methods (as evidenced by a degree in physics and astronomy, medical science, computer science, or mathematics, for instance) including the ability to write software (or willingness to learn it). 

Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS) (https://www.cardiff.ac.uk/study/international/english-language-requirements)

To apply, please visit the CDT website http://cdt-aimlac.org/ and follow the instructions 

Applicants should apply to the Doctor of Philosophy in Physics and Astronomy with a start date of 1st October 2021.

Applicants should submit an application for postgraduate study via the Cardiff University webpages (https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/physics-and-astronomy) including:

• an upload of your CV

• a personal statement/covering letter

• two references

• Current academic transcripts

In the research proposal section of your application, please specify the project title and supervisors of this project. If you are applying for more than one project, please list the individual titles of the projects in the text box provided. In the funding section, please select ’I will be applying for a scholarship/grant’.

To complete your application please email a pdf(s) of your application to


Funding Notes

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,285 per annum and additional funding for training, research and conference expenses.
The scholarships are open to UK/home and international candidates.
For general enquiries, please contact Rhian Melita Morris:

References

Real-Time Detection of Unmodeled Gravitational-Wave Transients UsingConvolutional Neural Networks
Vasileios Skliris, Michael R. K. Norman, Patrick J. Sutton
https://arxiv.org/abs/2009.14611

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