Week 6: Calibrating the Model

This week I focused on finalizing the ringdown pipeline for the first event GW150914 and then adapting the same structure to the rest of the events. At first, the pipeline returned a fitted frequency that was too far from the expected Kerr value and a damping time that was too large, which suggested that the fit was following detector noise instead of the actual ringdown signal. The problem seemed to come from the preprocessing stage since the original bandpass range and ringdown window were both too wide, so the fit still included low-frequency noise and too much late-time decay. After narrowing the bandpass to 100 to 400 Hz and shortening the ringdown window, the FFT (Fast Fourier Transform) peak moved much closer to the expected range of the Kerr ringdown frequency. 

The second issue was in the Bayesian step, where the likelihood was still comparing frequencies outside the relevant signal range. As a result, the fit could be pulled away from the true ringdown signal. I fixed this issue by restricting the likelihood to the same frequency range used in preprocessing. The data segment was also so short that the spacing between frequency points was too large to distinguish nearby frequencies well, so I zero-padded the data to make the sampled frequency grid denser. I also added PSD weighting so the fit relied more on cleaner parts of the detector frequencies.

After these changes, the event returned usable values that were close to the Kerr expectation. I applied the same corrected structure to GW170104, GW190814, GW170814, and GW151226, with several fixed parameters adjusted for each event. Next week I will test more events, compare values across events, and expand the results section.