I have rebuilt the MCMC simulation in an OOP fashion and incorporated Lance/Pytickle functionality into it. The usage of the MCMC now is much less messy, hopefully.

I made an example that calculates the closed-loop noise-coupling from SRCL sensing and displacement to DARM in A+. I used the control filters that Kevin defined in his controls example.

The resulting noise budget is in attachment 1. The code is in the 40m/bhd git.

I also investigated why aLIGO simulations behave so different than the A+ simulation (See few previous elogs in this thread). That is why aLIGO results are much less variable, and the simulations in aLIGO barely pass the validity checks, while A+ simulations almost always pass.

The way I check for the validity of a kat model is by scanning all the DOFs and checking that the corresponding sensing RFPDs demodulated signals cross zero. Attachment 2 shows these scanning for 3 such RFPDS for 3 cases:

A+ model with maxtem = 2

ALigo model with maxtem = 2

ALigo model with maxtem = 'off'

It seems like the scanning curves for A+ and ALigo with no HOMs are well behaved and look like normal PDH signals, while the ALigo with maxtem = 2 curves look funky. I believe that the aLIGO+HOMS curves indicate that the IFO is not really in a good locking point. All the IFO lockings were done by using the locking methods straight out of the PyKat package.