Design of novel signal processing algorithms for the detection and reconstruction of gravitational waves emitted by core-collapse supernovae

Since the first detection of gravitational waves (GWs) by the LIGO detectors of a binary black hole merger in 2015, many more binary mergers have been detected. A new generation of GW detectors will be build, whose improved sensitivity will allow the detection of core-collapse supernovae (CCSNe). Those detections can result in a better understanding of CCSNe, interaction between the fundamental forces of nature in extreme environments and astrophysical processes that depend on CCSNe such as nucleosynthesis. New algorithms are required because CCSNe waveforms are estimated through various simulations and are stochastic. The proposal aims to develop these new algorithms by applying state-of-the art digital signal processing principles. The null-stream analysis will be repurposed to detect GWs with low latency and minimal assumptions on the source while deterministic section of the GW will be targetted by a template-based search. The maximal amount of information will be extracted from the waveform by exploiting the multi-messenger structure of CCSNe. A generalised sidelobe cancellation (GSC) structure will attempt to exploit the information in the null-stream for noise reduction. An optimal wavelet transformation will be selected to improve the reconstruction of the GW. Extensive simulations will be performed to investigate the maximal source distance of the proposed algorithms and compare them to the state-of-the-art.