The second project described in this thesis is development of a computationally inexpensive test that can be run quickly over LIGO data to flag times where data have been corrupted by a nonlinear coupling. The test is applied to LIGO data and is shown to flag segments whose bispectra contains similar features to the bispectra of data produced with a nonlinear model.
Finally, the third project seeks to address two problems that one would confront if one tried to do core-collapse supernova astronomy with gravitational waves. The first problem involves extracting a short-duration gravitational waveform from the data produced by a network of detectors. The maximum entropy method is proposed as a solution to this deconvolution problem. The second problem involves deducing properties of the source from the recovered waveform when our source models are incomplete. We propose calculating the cross correlation between a recovered waveform and a catalog of waveforms associated with models having varying properties. The catalog waveform having the highest cross correlation with the recovered waveform is assumed to be associated with a model whose properties most closely resemble those of the source. The maximum entropy method is used to recover supernova waveforms from simulated LIGO data which are created assuming detector responses and white noise having amplitudes typical of recent LIGO science runs. Next, the recovered waveform is cross correlated with a catalog of waveforms and it is shown that the recovered waveform carries information about the type of bounce the core undergoes as well as the progenitor mass, angular momentum and degree of differential rotation for supernova occurring less than a few kpc away. Supernova waveforms are also recovered using maximum entropy from simulated data using actual LIGO data for noise and from hardware injections. Recovering signals from these data show that maximum entropy can successfully handle colored noise and imperfect knowledge of the LIGO detector responses.