Demonstrating Measurement to Model residual systematic error.
I have plotted the diffference between observed data of the UIM L2L transfer function in comparison with the following models:
A Pure 1 / f^6 TF
CALCS filter before 20200113
pyDARM model from 20190909 params file
CALCS filter after 20200113
pyDARM model from 20190103 params file
The order is chesen to be in decending order with increasing "goodness" - this way to best one is plotted on top of the rest
The plots are broken up into 5 parts of the freuquency spectrum (where we have data: 5 to 550 Hz), to make the lare amount of infromation more readable.
Going through these plots is informative in understanding how each model is "good" is various parts of the spectrum, and how it can be "bad" in other parts.
The take home message is that the new pyDARM model is good up to 200 Hz, and the new CALCS filter is approximately identical to it, and reflects the data just as well.
Images attached to this comment
vladimir.bossilkov@LIGO.ORG - 09:18, Wednesday 15 January 2020 (54521)
Demonstrate Model to CALCS residual systematic error.
I had plotted this with MATLAB before, but for improved clarity I'll plot the CALCS differences from pyDARM for all stages L1,L2 and L3 (UIM, PUM, TST) L2L transfer functions: so that it is clear where the models are good and bad when we fit these models into the front end filter banks.
The most drastically different is the L1 stage (which the comment preceeding this claims to be a pretty close fit to observed data). You can see the effect of the removal of a vast amount of poles and zeroes from the TF impacting the low frequency, as well as the two missing features at 90 Hz and 134 Hz, also visible in the previous comment.
At high frequency, gain and phase difference comes about from the fact that the front end model is a discrete model (with a sample rate of 16384) and hence has a Nyquist frequency around 8kHz, so what you see is the contributing fraction of that coming into lower frequencies.
Images attached to this comment
vladimir.bossilkov@LIGO.ORG - 09:21, Wednesday 15 January 2020 (54522)
Remake the "contributions to R" plot, using the new 20200103 parameter file.
Now that the paramter file is all its detail about it updated, the previous estimates to the contribution of that 152 Hz feature to the response can be more accurately modelled.
Where I previously said that the contribution was about 8.5%, it is clear that it is closer to 7%.
The H1CALCS filter banks were rearranged to make more sense and to make it obvious that there is plently of room to add more filter bank blocks that would do an even *better* job of representing the full transfer functions in the front end.
Attached are the images so that you can see how things are arranged. Below I define what the abbreviations mean:
susn_RB* : Normalised suspension model for Rigid Body dynamics.
susn_BD*: Normalised suspension model for (UIM) Blade Dynamics.
susn_VM: Normalised suspension model for Violin Mode dynamics.
mpN_20Hz: meters per Newton - this is simply the overall gain of the L2L transfer functions, and is measured at a reference frequency of 20Hz where the front end transfer function is by definition exactly the same is the one for the pyDARM model.
Npct_O3B: Newton per count parameter reflecting the 20200103 model
actsing: 1 or -1 to set polarity of actuation
armsign: 1 or -1 to set polarity of actuation depending on which arm is being actuated on
1/f^* : the 1/f^n (n=2,4,6) roll off transfer fucntion that is removed from the normalised suspension models during their calculation.
HFpole: extra TF for the TST stage to account for Parametric Instability actuators/dampers affecting TF at much higher frequency
biassign: -1 for some other exotic polarity setting issue in the TST stage
Images attached to this comment
vladimir.bossilkov@LIGO.ORG - 14:57, Friday 17 January 2020 (54562)
minor clarification on the biassign, in the above comment, since I was in a rush and not thinking: its the bias on the ESD drive for actuation on the TST stage
vladimir.bossilkov@LIGO.ORG - 14:54, Tuesday 21 January 2020 (54631)
Also as a backup I've included the files from this directory as an attachment. I had to omitted the very densely evaluated transfer function from foton that was used to produce plots in the first set of figures, because it makes the attachment too large. You can probably use ETMX_L1 new and old files used in the second set of files without much error.
While writing the O3A cal paper, I made a comparison plot showing the UIM contributions in 0909 O3A, and the 0909 O3A model with O3B SUS data (trunk/Common/pyDARM/matlab_scripts/20200107_H1_EX_O3_susdata.mat).
The comparison is show in the attached pdf. The "old UIM" is the actual 0909 model. The "new UIM" is 0909+O3B sus.
Demonstrating Measurement to Model residual systematic error.
I have plotted the diffference between observed data of the UIM L2L transfer function in comparison with the following models:
The order is chesen to be in decending order with increasing "goodness" - this way to best one is plotted on top of the rest
The plots are broken up into 5 parts of the freuquency spectrum (where we have data: 5 to 550 Hz), to make the lare amount of infromation more readable.
Going through these plots is informative in understanding how each model is "good" is various parts of the spectrum, and how it can be "bad" in other parts.
The take home message is that the new pyDARM model is good up to 200 Hz, and the new CALCS filter is approximately identical to it, and reflects the data just as well.
Demonstrate Model to CALCS residual systematic error.
I had plotted this with MATLAB before, but for improved clarity I'll plot the CALCS differences from pyDARM for all stages L1,L2 and L3 (UIM, PUM, TST) L2L transfer functions: so that it is clear where the models are good and bad when we fit these models into the front end filter banks.
The most drastically different is the L1 stage (which the comment preceeding this claims to be a pretty close fit to observed data). You can see the effect of the removal of a vast amount of poles and zeroes from the TF impacting the low frequency, as well as the two missing features at 90 Hz and 134 Hz, also visible in the previous comment.
At high frequency, gain and phase difference comes about from the fact that the front end model is a discrete model (with a sample rate of 16384) and hence has a Nyquist frequency around 8kHz, so what you see is the contributing fraction of that coming into lower frequencies.
Remake the "contributions to R" plot, using the new 20200103 parameter file.
Now that the paramter file is all its detail about it updated, the previous estimates to the contribution of that 152 Hz feature to the response can be more accurately modelled.
Where I previously said that the contribution was about 8.5%, it is clear that it is closer to 7%.
Make the "final" version of R_foton_new / R_foton_old plot.
With the parameter file update fully, this plot also changes from the one I plotted previously.
This plot is generated by:
This was the expected change in DELTALEXT/PCAL. The actual observed change was here,with further discussion.
The H1CALCS filter banks were rearranged to make more sense and to make it obvious that there is plently of room to add more filter bank blocks that would do an even *better* job of representing the full transfer functions in the front end.
Attached are the images so that you can see how things are arranged. Below I define what the abbreviations mean:
minor clarification on the biassign, in the above comment, since I was in a rush and not thinking: its the bias on the ESD drive for actuation on the TST stage
To find the code to generate the figures in this alog, consult the following directory:
/ligo/home/vladimir.bossilkov/Work_Done/20200111_calibration_checks
Also as a backup I've included the files from this directory as an attachment. I had to omitted the very densely evaluated transfer function from foton that was used to produce plots in the first set of figures, because it makes the attachment too large. You can probably use ETMX_L1 new and old files used in the second set of files without much error.
While writing the O3A cal paper, I made a comparison plot showing the UIM contributions in 0909 O3A, and the 0909 O3A model with O3B SUS data (trunk/Common/pyDARM/matlab_scripts/20200107_H1_EX_O3_susdata.mat).
The comparison is show in the attached pdf. The "old UIM" is the actual 0909 model. The "new UIM" is 0909+O3B sus.