Week 2: Adjusting Parameters

This week I was mainly running analysis code on GW150914, the first event. I spent most of my time downloading packages and debugging. My first attempt at modeling the ringdown with a damped sinusoid gave an R² of about 0.2, meaning the model barely matched the data. The frequency I found was 113 Hz, but the expected Kerr prediction is around 251 Hz. Additionally, the damping time was unrealistically large.

A systematic review of the preprocessing stage identified two sources of error. First, the bandpass filter starts at 30 Hz. That let low-frequency detector noise into the analysis and dominated the FFT. I changed the filter to a range of 100 to 400 Hz to isolate the signal better. Second, the initial ringdown window was defined at 50 ms. Given that the signal typically decays within 10 ms, the model was attempting to fit a damped oscillation to 40 ms of stochastic noise. This necessitated a reduction of the analysis window to prevent the fitting algorithm from converging on non-physical parameters.

After fixing these issues, I moved from simple curve fitting to a full Bayesian inference using the bilby library and the dynesty sampler. This let me create probability distributions and credible intervals for the frequency and damping time. To ensure the robustness of the results, the analysis was executed across three distinct ringdown start times of 3 ms, 5 ms, and 10 ms post-merger, respectively.

Next week I plan to finalize the corrected PSD-weighted Bayesian analysis for GW150914 and verify that the measured frequency and damping time are consistent with the Kerr prediction. Once it’s working, I will apply it to the second event, GW170104, which has a lower remnant mass and therefore a different predicted ringdown frequency, to check whether the analysis generalizes across events.