Week 5: Calibrating the Code

This week I fixed a lot of errors in the code and found out why the analysis was returning incorrect figures for the first event. I restarted the Bayesian process, however, the first part of the code failed immediately. I fixed this by obtaining the data in a different format from the Gravitational Wave Open Science Center and adjusting the parameter in the first cell. After it worked, another procedure was identified with a fitting error. The code returned a frequency of 323 Hz, which was higher than the expected frequency of ~251 Hz. This happens since the likelihood function did not add all frequency bins correctly. The data were in the range of 100 to 400 Hz, but the likelihood function looked at all the bins up to 2048 Hz. Since the signal has width in terms of frequency, the signal overflowed over the margins of the 100 to 400 Hz band, and the likelihood function identified this spillover as a bad fit and penalized it. This made the final results a bit inaccurate, as the algorithm chose higher frequencies and longer damping durations since those characteristics helped stay in the filter better.

Another issue was how well the code could distinguish the difference between nearby frequencies, as it could only identify frequencies apart in 50 Hz jumps with 20 milliseconds of data (82 data points). The fit lacked sufficient information to figure out the appropriate solution since it only had 7 slots to work with and 4 unknowns to solve for. I fixed this by adding zeros to the data to make it 512 points long, which helped the math zoom in on the frequency spectrum and give more slots to work with. I also changed the code to assess how well a model matches the data by adjusting the comparison range to where the data is actually meaningful rather than limiting it to the 100 to 400 Hz range.