Here is an updated plot - the main difference is that I have added a trace that is the frequency domain wiener filter subtraction of the coherent power between the L_X and L_Y time series. I tried reproducing the calculation with the time domain wiener filter subtraction as well, using half of the time series (i.e. 5 mins) to train the wiener filter (with L_X as target and L_Y as witness), but I don't get any subtraction above 5 Hz on the half of the data that is a test data set. Probably I am not doing the pre-filtering correctly - I downsampled the signal to 1 kHz, weighted it by low passing the signal above 40 Hz and trained the Wiener filter on the resulting time series. But this frequency domain Wiener filter subtraction should be at least a lower bound on DARM - indeed, it is slightly lower everywhere than simply taking the time domain subtraction of the two data streams.

To do:

• Re-measure calibration numbers used.
• Redo calculation once the numbers have been verified.

Putting a slightly cleaned up version of this plot in now - I'm only including the coherence-inferred DARM estimate now instead of the straight up time domain subtraction. So this is likely to be an underestimate. At low (<10 Hz) frequencies, the time domain computation lines up fairly well, but I suspect that I am getting huge amounts of spectral leakage (see Attachment #2) in the way I compute the spectrum using scipy's filtering routine (once the Wiener filter has been computed). Note that Attachment #2, I didn't break up the data into a training/testing set as in this case, we just care about the one-off offline performance in order to get an estimate of DARM.

The python version of the wiener filter generating code only supports [b,a] output of the digital filter, an sos filter might give better results. Need to figure out the least painful way of implementing the low-noise digital filtering in python...