- Fast transients and pulsars
Discovering new pulsars and exploiting them for new physics and astrophysics has been a rich tradition of radio astronomy. With pulsar astronomy transitioning to next-generation radio arrays (e.g., the MWA) it may be possible to rapidly localise any promising pulsar candidates interferometrically, while continuing to employ traditional time-domain search techniques for efficient identification of such candidates. The ability to localise promptly (and precisely) brings the benefit of accelerated convergence to pulsar’s astrometry, besides facilitating their prompt follow-ups with other telescopes. However, to date, the viability of such an approach has not been demonstrated.
An obvious approach to direction finding is by forming a beam and performing a search over the field of interest. This approach, however, is limited by the resolution of the beam which is ultimately limited by the size of the array. An alternative approach which is not limited by the beamwidth is through analysis of the eigenvalues and eigenvectors of the covariance matrix. The signal subspace is represented by the signal eigenvectors while the noise eigenvectors span the noise subspace. The direction of the signal is found by projecting a search direction onto the noise subspace. If the search direction coincides with a signal direction we will detect a zero value.
This method has the potential to find the direction of the pulsar with much sharper resolution than the beam size. It also has an application in localising RF interference sources, in particular those near the horizon as radio astronomy pixel sizes become too large at low elevation angles. However, the algorithm needs reasonable estimates of the receiver noise and background noise. The aim of the project is to develop greater understanding of these subtleties by processing data with known pulsars in the field of view.
4th year BEng (Hons.) or BSc. (Hons.) student in electrical engineering, physics and applied mathematics.
High-level programming languages such as Python, MATLAB and some UNIX experience.
The student will be guided in data access procedure and data processing.
- Week 1 Inductions and project introduction
- Week 2 Initial Presentation
- Week 3 Background research
- Week 4 Process simulated data
- Week 5 Design algorithm
- Week 6 Process observation data
- Week 7 Process observation data
- Week 8 Summarise results
- Week 9 Final Presentation
- Week 10 Final Report
Dr Ramesh Bhat
Senior Research FellowRead More