Frequency domain reduced order model of aligned-spin effective-one-body waveforms with generic mass-ratios and spins

I provide a frequency domain reduced order model (ROM) for the aligned-spin effective-one-body (EOB) model "SEOBNRv2" for data analysis with second and third generation ground based gravitational wave (GW) detectors. SEOBNRv2 models the dominant mode of the GWs emitted by the coalescence of black hole (BH) binaries. The large physical parameter space (dimensionless spins $-1 \leq \chi_i \leq 0.99$ and symmetric mass-ratios $0.01 \leq \eta \leq 0.25$) requires sophisticated reduced order modeling techniques, including patching in the parameter space and in frequency. I find that the time window over which the inspiral-plunge and the merger-ringdown waveform in SEOBNRv2 are connected is discontinuous when the spin of the deformed Kerr BH $\chi=0.8$ or the symmetric mass-ratio $\eta \sim 0.083$. This discontinuity increases resolution requirements for the ROM. The ROM can be used for compact binary systems with total masses of $2 M_\odot$ or higher for the advanced LIGO (aLIGO) design sensitivity and a $10$ Hz lower cutoff frequency. The ROM has a worst mismatch against SEOBNRv2 of $\sim 1\%$, but in general mismatches are better than $\sim 0.1\%$. The ROM is crucial for key data analysis applications for compact binaries, such as GW searches and parameter estimation carried out within the LIGO Scientific Collaboration (LSC).Comment: 14 pages, 14 figure

Directional edge and texture representations for image processing

An efficient representation for natural images is of fundamental importance in image processing and analysis. The commonly used separable transforms such as wavelets axe not best suited for images due to their inability to exploit directional regularities such as edges and oriented textural patterns; while most of the recently proposed directional schemes cannot represent these two types of features in a unified transform. This thesis focuses on the development of directional representations for images which can capture both edges and textures in a multiresolution manner. The thesis first considers the problem of extracting linear features with the multiresolution Fourier transform (MFT). Based on a previous MFT-based linear feature model, the work extends the extraction method into the situation when the image is corrupted by noise. The problem is tackled by the combination of a "Signal+Noise" frequency model, a refinement stage and a robust classification scheme. As a result, the MFT is able to perform linear feature analysis on noisy images on which previous methods failed. A new set of transforms called the multiscale polar cosine transforms (MPCT) are also proposed in order to represent textures. The MPCT can be regarded as real-valued MFT with similar basis functions of oriented sinusoids. It is shown that the transform can represent textural patches more efficiently than the conventional Fourier basis. With a directional best cosine basis, the MPCT packet (MPCPT) is shown to be an efficient representation for edges and textures, despite its high computational burden. The problem of representing edges and textures in a fixed transform with less complexity is then considered. This is achieved by applying a Gaussian frequency filter, which matches the disperson of the magnitude spectrum, on the local MFT coefficients. This is particularly effective in denoising natural images, due to its ability to preserve both types of feature. Further improvements can be made by employing the information given by the linear feature extraction process in the filter's configuration. The denoising results compare favourably against other state-of-the-art directional representations