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Entry  Thu Aug 6 00:34:38 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 8x
    Reply  Fri Aug 7 11:23:47 2020, rana, Update, BHD, Monte Carlo Simulations 
       Reply  Mon Aug 10 07:13:00 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 8x
          Reply  Tue Aug 25 05:51:29 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 8x
             Reply  Mon Sep 14 07:50:01 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 10x
                Reply  Fri Oct 16 09:16:37 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 8x
                   Reply  Thu Oct 22 11:48:08 2020, Yehonathan, Update, BHD, Monte Carlo Simulations 8x
                      Reply  Thu Dec 10 14:48:00 2020, Yehonathan, Update, BHD, Monte Carlo Simulations MCMCLance_NoiseBudget_Example.pdfIFO_Check.pdf
                         Reply  Fri Dec 11 09:28:52 2020, rana, Update, BHD, Monte Carlo loop coupling Simulations 
                            Reply  Mon Dec 14 11:09:28 2020, Yehonathan, Update, BHD, Monte Carlo loop coupling Simulations MCMC_LANCE_OLTFs.pdfMCMC_LANCE_OLCoupling2DARM.pdf
                               Reply  Mon Jan 11 16:11:51 2021, Yehonathan, Update, BHD, Monte Carlo loop coupling Simulations MC_LANCE_OLTFs.pdf
                                  Reply  Mon Jan 11 19:10:10 2021, rana, Update, BHD, Monte Carlo loop coupling Simulations 
Message ID: 15727     Entry time: Thu Dec 10 14:48:00 2020     In reply to: 15637     Reply to this: 15732
Author: Yehonathan 
Type: Update 
Category: BHD 
Subject: Monte Carlo Simulations 

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. 

Attachment 1: MCMCLance_NoiseBudget_Example.pdf  63 kB  | Hide | Hide all
MCMCLance_NoiseBudget_Example.pdf
Attachment 2: IFO_Check.pdf  22 kB  | Hide | Hide all
IFO_Check.pdf
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