I’m working on a code to determine the Gaussianity of a PSD.
It can be difficult to distinguish between GW events and non-Gaussian noise, especially in burst searches. By characterizing noise Gaussianity, we can better recognize noise patterns and distinguish between GW events and noise.
What I did:
I analyzed an hour of S5 L1 data. First, I plotted a timeseries, just to see what I was working with. Then, I produced a PSD (technically, an ASD) for the timeseries using Welch’s method in Python.
I split the data segment into smaller time-chunks and then produced a PSD for each chunk. All PSDs were superimposed in one plot. Here’s a plot for 201 time-chunks of equal length:
For a specific frequency, I can view the spread in PSD value through the production of a histogram.
I’ve made histograms displaying varying PSD values for the 201 PSD plot at 100 Hz, 500 Hz, and 1kHz.
For Gaussian noise, an exponential decay plot is expected. I will continue this analysis by following the statistical method in Ando et al. 2003 to calculate specific values indicative of the Gaussianity of various distributions. I’ll then look at different periods of time in the S5 L1 data to find periods of time suggesting non-Gaussian behavior.