Current Research
The Earth is just a small small space in the immense Universe. There are millions and billions of stars out there. An isolated star will eventually explode in what is known as a supernova during which a tremendous amount of energy is release. Depending on the mass a compact object will be formed. These supernovae have been observed throughout history. Most massive stars, however, do not evolve in isolation.
They have one or more companions that will disrupt and add funk to their evolution leading to supernovae with different characteristics and even the creation of binary compact object systems. A merger of a compact object binary was detected in September 2015 with the first direct observation of gravitational waves using the LIGO and VIRGO detectors. This provides an exciting new way to observe the universe and is an excellent test for general relativity. At the same time it provides an insight in the creation and evolution of binary systems.
In my PhD research I am combining large scale cosmological simulations, such as the Millennium simulation, with a detailed binary populations synthesis code - BPASS - to estimate the expected event rates of different supernovae and other transient events, such as compact object mergers. For each transient event we will be able to provide properties of their host galaxies. It is even possible to identify a spectral fingerprint due to the presence of spectral synthesis in BPASS. This could help constraint the optical search for gravitational wave events. Other observables might also be possible and at the moment we’re looking into those possibilities.
Past research:
KM3NeT
The KM3NeT collaboration aims to solve the unknown neutrino mass ordering and searches for cosmic neutrinos.
At a depth of over 2 kilometres in the Mediterranean Sea, it uses PMTs to record Cherenkov radiation from the high-energy neutrino reaction products.
A muon neutrino will leave a long path of photon hits on the PMT through the detector.
The direction of the neutrino is linked to the amount of matter traversed through the Earth, which in turn provides information about the neutrino mass ordering. The direction of the track is approximated in a process called reconstruction.
It uses the information of the photon hits and the PMT’s properties in a two step process.
The first generates a set of starting values for the second algorithm.
It uses a maximum likelihood method to find the best fitting track to the data.
As an initial focus of my research I analysed the current algorithms and implemented
a new intermediate algorithm with parameterisations of the hit information, which significantly
improves the accuracy of all track guesses.
In my Master Thesis I also introduced a likelihood using more of the available detector information. Its likelihood space, which used the hit and no-hit information, shows potential and minimisation with the true parameters leads to a significant improvement in the mean directional error from 1.92° to 0.81° compared to the current main reconstruction. Furthermore, 60% of the reconstructed tracks are now at a sub-degree accuracy. The likelihood space does contain many local minima and the pre-reconstruction is not optimised for this comprehensive likelihood putting it at a disadvantage. With further optimisation, the extended could provide better constraints on the direction and the neutrino mass ordering.