Seen ~0.1 seconds before in LSC-DARM-IN1.

Powered OFF at 22:19:30 UTC.

The only SDF diffs were some TRAMPS that I think were left over from earlier activities.

A reminder that there is a delay of several seconds  between pressing the On and Off buttons and the WAP transitioning to the requested state.

Attached is more recent data.  This is troubling to me.  Upon further consideration the "ice" theory isn't applicable because of the time duration of the pressure anomaly.  Chandra R. has had a long standing request to connect the RGA at the MY into the network but, for now, we can't "see" the partial pressures. 

After looking at trend data in seconds, we see the pressure rose at corner station first.

Here is a seven day pressure and temperature trend in mid-Y VEA. No surprise that as temperature decreases, so does pressure. However, doesn't explain the initial abnormal rise in pressure we saw on Tuesday evening. Let's keep an eye on this, and I will work with CDS team to connect RGA for remote access. 

Updated yaw sensing matrix, with DHARD & CHARD inputs increased by a factor of 3:

ASC Sensing Matrix (YAW), [W/rad]

The whitening / anti-whitening gains in runAnalysis_vII.py were outdated. Attached are the tables with corrected gains, and also in raw cts/rad.

The first observation ready segment with the updated calibration model start just now at Jan 14 2020 00:47:59 UTC, or GPS 1262998097.

Attached are plots of GDS data during the broadband injection, as well as plots showing how well the filters represent the frequency-domain DARM model.  The broadband injection (first plot) looks good, showing deviations no greater than ~2% from 20 Hz - 350 Hz.  The last plot shows how well the low-latency (front-end + GDS) calibration pipeline applies the response function R(f).  The "spike" seen at 4276.0 Hz is not modeled at all by the filters.  The source of this in the model is in all 3 stages of the actuation (only TST and PUM contribute significantly to the response function), and I assume it is a violin mode.  This is a very narrow freature in the model, no wider than 0.25 Hz.  Models for TST, PUM, and UIM all rise 12 or 13 orders of magnitude at this very narrow feature.  We can attempt to model this in the inverse sensing path (which has a high enough sample rate), but it won't be modeled very well if we try, since it is such a narrow feature.  Moreover, this would also compromise accuracy in neighboring frequency bins.  Most likely, there will still be a loud spectral line in h(t) at 4276 Hz, and the systematic error induced by using the current filters would be that this line appears 3 orders of magnitude lower than it actually is.

Here's a comparison between GDS-CALIB_STRAIN's response to a broadband PCAL Y injection before vs. after this model update. Assuming PCAL is a perfect reference, this should be equivalent to a direct measure of the systematic error in the response function and h(t).

One can see that, while we've cleaned up the UIM feature at 153 Hz, and improved the response ratio below 30 Hz, we seemed to made the systematic error worse between 60 and 150 Hz. 

In the first attachment, I show each of the transfer functions on top of each other, to show the former vs. the current level of systematic error.
In the second attachment, I show the ratio of the two transfer functions, to show the *change* in systematic error. 

This second attachment should correspond to what Vlad predicted in LHO aLOG 54523. It's close... but not quite right. 

Still investigating...

The script used to make this plot can be found here:
    /ligo/svncommon/CalSVN/aligocalibration/trunk/Runs/O3/H1/Scripts/FullIFOSensingTFs/
        plot_GDS_BB_20200115.py
and relies on data processed by Aaron and committed to 
    /ligo/svncommon/CalSVN/aligocalibration/trunk/Runs/O3/H1/Results/GDS_BB_plots/
        H1_C00_over_CAL-PCALY_RX_PD_OUT_DQ_1262638969-178.txt
        H1_C00_over_CAL-PCALY_RX_PD_OUT_DQ_1262990871-153.txt

I did some empirical probing into what could be giving this kind of responce between 20 and 300 Hz.

For this I created a new copy of the modelparams_H1_20200103.py file to play with.

The plot attached here, is where I have taken the ratio of my new version over the currently used version, but I have altered:

• ccOpticalGain is mulitplied by the current Kappa_C written out in the frontend (0.996)
• ccPoleFreq is the current cavity pole frequency in the front end (has minimal effect in this frequency range, but "corrects" for the response change from optical gain at high frequency.

This is plotted against the very data in the above comment, for reference.

It looks like the current systematic error trend can be ?just about? completely explained by this correction! It seems response in this range is *extremely* sensitive to the value of ccOpticalGain in the model.

EDIT: spoke to Jeff - more convincing to reanalyse this w.r.t the Orange line in his figures in the previous comment, and see if it explains the complete error in response.

For reference, here are the values of the TDCFs that were applied to the data in the GDS pipeline during the broadband injections.

During the injection starting at 1262638969:

kappa_tst = 1.0052612

kappa_pum = 1.0198756

kappa_uim = 0.99628365

kappa_C = 0.99136031

f_cc = 411.13184 Hz

During the injection starting at 1262990871:

kappa_tst = 0.99716723

kappa_pum = 1.017796

kappa_uim = 0.99592042

kappa_C = 0.99556768

f_cc = 410.88696 Hz

We needed a better understanding of the impact of this systematic error at ~150 Hz. for the UIM, so I added a copy of ratio plot from LHO aLOG 54565 to the same script,
    ^/trunk/Runs/O3/H1/Scripts/FullIFOSensingTFs/plot_GDS_BB_20200115.py 
and zoomed in around the 100-200 Hz frequency region.

Attached are the results. 

(1) We're, of course, limited by the frequency resolution and noise of the measurement, BUT,
(2) We see what Vlad has told us all along: there are actually three features: highQ anti-resonance at 151 Hz, highQ anti-resonance at 153 Hz, and then a high Q resonance at 154 Hz (rounding to the nearest Hz). Note that this description is of the *ratio* of (fixed) / (not fixed), so take my description of whether the feature is a "resonance" vs. "anti-resonance" with a grain of salt.
(3) Each highQ feature peaks at around a -2%, -3%, and +3%, BUT -- that includes influence from the "underlying" broad frequency dependent error "sweeping through" this region -- known to be a result of problems with the TST actuator model in this low-latency data.