ELOG 40m

Using ALS beatnote for calibration, test

Test details:

We locked both arms and opened the shutter for Yend green laser.
After toggling the shutter on.off, we got a TEM00 mode of green laser locked to YARM.
We then cleared the phase Y history by clicking "CLEAR PHASE Y HISTROY" on C1LSC_ALS.adl (opened from sitemap > ALS > ALS).
We sent excitation signal at ITMY_LSC_EXC using awggui at 43Hz, 77Hz and 57Hz.
We measured the power spectrum and coherence of C1:ALS-BEATY_FINE_PHASE_OUT_HZ_DQ and C1:SUS-ITMY_LSC_OUT_DQ.
The BEATY_FINE_PHASE_OUT_HZ is already calibrated in Hz. This we assume is done by multip[lying the VCO slope in Hz/cts to the error signal of the digital PLL loop that tracks the phase of beatnote.
We calibrated C1:SUS-ITMY_LSC_OUT_DQ by multiplying with
$\large 3 \times \frac{2.44 \, nm/cts}{f^2} \times \frac{c}{1064\,nm \times 37.79\, m} = \frac{54.77}{f^2} kHz/cts$ where f is in Hz.
The 2.44/f² nm/cts is taken from 13984.
We added the calibration as Poles/zeros option in diaggui using gain=54.577e3 and poles as "0, 0".
We found that ITMY_LSC_OUT_DQ calibration matches well at 57Hz but overshoots (80 vs 40) at 43 Hz and undershoots (50 vs 80) at 77Hz.

Conclusions:

If we had DRFPMI locked, we could have used the beatnote spectrum as independent measurement of arm lengths to calibrate the interferometer output.
We can also use the beatnote to confirm or correct the ITM actuator calibrations. Maybe shape is not exactly 1/f² unless we did something wrong here or the PLL bandwidth is too short.

16315

Tue Sep 7 18:00:54 2021

Tega

System Identification via line injection

[paco]

This morning, I spent some time restoring the jupyter notebook server running in allegra. This server was first set up by Anchal to be able to use the latest nds python API tools which is handy for the calibration stuff. The process to restore the environment was to run "source ~/bashrc.d/*" to restore some of the aliases, variables, paths, etc... that made the nds server work. I then ran ssh -N -f -L localhost:8888:localhost:8888 controls@allegra from pianosa and carry on with the experiment.

[paco, hang, tega]

We started a notebook under /users/paco/20210906_XARM_Cal/XARM_Cal.ipynb on which the first part was doing the following;

Set up list of excitations for C1:LSC-XARM_EXC (for example three sine waveforms) using awg.py
Make sure the arm is locked
Read a reference time trace of the C1:LSC-XARM_IN2 channel for some duration
Start excitations (one by one at the moment, ramptime ~ 3 seconds, same duration as above)
Get data for C1:LSC-XARM_IN2 for an equal duration (raw data in Attachment #1)
Generate the excitation sine and cosine waveforms using numpy and demodulate the raw timeseries using a 4th order lowpass filter with fc ~ 10 Hz
Estimate the correct demod phase by computing arctan(Q / I) and rerunning the demodulation to dump the information into the I quadrature (Attachment #2).
Plot the estimated ASD of all the quadratures (Attachment #3)

[paco, hang, tega]

Estimation of open loop gain:

Grab data from the C1:LSC-XARM_IN1 and C1:LSC-XARM_IN2 test points
Infer excitation from their differnce, i.e. C1:LSC-XARM_EXC = C1:LSC-XARM_IN2 - C1:LSC-XARM_IN1
Compute the open loop gain as follows : G(f) = csd(EXC,IN1)/csd(EXC,IN2), where csd computes the cross spectra density of the input arguments
For the uncertainty in G, dG, we repeat steps (1) to (3) with & without signal injection in the C1:LSC-XARM_EXC channel. In the absence of signal injection, the signal in C1:LSC-XARM_IN2 is of the form: Y_ref = Noise/(1-G), whereas with nonzero signal injection, the signal in C1:LSC-XARM_IN2 has the form: Y_cal = EXC/(1-G) + Noise/(1-G), so their ratio, Y_cal/Y_ref = EXC/Noise, gives the SNR, which we can then invert to give the uncertainty in our estimation of G, i.e dG = Y_ref/Y_cal.
For the excitation at 53 Hz, our measurtement for the open loop gain comes out to about 5 dB whiich is consistent with previous measurement.
We seem to have an SNR in excess of 100 at measurement time of 35 seconds and 1 count of amplitude which gives a relative uncertainty of G of 0.1%
The analysis details are ongoing. Feedback is welcome.

16352

Tue Sep 21 11:13:01 2021

XARM calibration noise

Here are some plots from analyzing the C1:LSC-XARM calibration. The experiment is done with the XARM (POX) locked, a single line is injected at C1:LSC-XARM_EXC at f0 with some amplitude determined empirically using diaggui and awggui tools. For the analysis detailed in this post, f0 = 19 Hz, amp = 1 count, and gain = 300 (anything larger in amplitude would break the lock, and anything lower in frequency would not show up because of loop supression). Clearly, from Attachment #3 below, the calibration line can be detected with SNR > 1.

We read the test point right after the excitation C1:LSC-XARM_IN2 which, in a simplified loop will carry the excitation suppressed by 1 - OLTF, the open loop transfer function. The line is on for 5 minutes, and then we read for another 5 minutes but with the excitation off to have a reference. Both the calibration and reference signal time series are shown in Attachment #1 (decimated by 8). The corresponding ASDs are shown in Attachment #2. Then, we demodulate at 19 Hz and a 30 Hz, 4th-order butterworth LPF, and get an I and Q timeseries (shown in Attachment #3). Even though they look similar, the Q is centered about 0.2 counts, while the I is centered about 0.0. From this time series, we can of course show the noise ASDs in Attachment #3.

The ASD uncertainty bands in the last plot are statistical estimates and depend on the number of segments used in estimating the PSD. A thing to note is that the noise features surrounding the signal ASD around f0 are translated into the ASD in the demodulated signals, but now around dc. I guess from Attachment #3 there is no difference in the noise spectra around the calibration line with and without the excitation. This is what I would have expected from a linear system. If there was a systematic contribution, I would expect it to show at very low frequencies.

16353

Wed Sep 22 11:43:04 2021

rana

XARM calibration noise

I would expect to see some lower frequency effects. i.e. we should look at the timeseries of the demod with the excitation on and off.

I would guess tat the exc on should show us the variations in the optical gain below 3 Hz, whereas the exc off would not show it.

Maybe you should do some low pass filtering on the time series you have to see the ~DC effects? Also, reconsider your AA filter design: how do you quantitatively choose the cutoff frequency and stopband depth?

16363

Tue Sep 28 16:31:52 2021

XARM OLTF (calibration) at 55.511 Hz

[anchal, paco]

Here is a demonstration of the methods leading to the single (X)arm calibration with its budget uncertainty. The steps towards this measurement are the following:

We put a single line excitation through the C1:SUS-ETMX_LSC_EXC at 55.511 Hz, amp = 1 counts, gain = 300 (ramptime=10 s).
With the arm locked, we grab a long timeseries of the C1:LSC-XARM_IN1_DQ (error point) and C1:SUS-ETMX_LSC_OUT_DQ (control point) channels.
We assume the single arm loop to have the four blocks shown in Attachment #1, A (actuator + sus), plant (mainly the cavity pole), D (detection + electronics), and K (digital control).
1. At this point, Anchal made a model of the single arm loop including the appropriate filter coefficients and other parameters. See Attachments #2-3 for the split and total model TFs.
2. Our line would actually probe a TF from point b (error point) to point d (control point). We multiplied our measurement with open loop TF from b to d from model to get complete OLTF.
3. Our initial estimate from documents and elog made overall loop shape correct but it was off by an overall gain factor. This could be due to wrong assumption on RFPD transimpedance or analog gains of AA or whitening filters. We have corrected for this factor in the RFPD transimpedance, but this needs to be checked (if we really care).
We demodulate decimated timeseries (final sampling rate ~ 2.048 kHz) and I & Q for both the b and d signals. From this and our model for K, we estimate the OLTF. Attachment #4 shows timeseries for magnitude and phase.
Finally, we compute the ASD for the OLTF magnitude. We plot it in Attachment #5 together with the ASD of the XARM transmission (C1:LSC-TRX_OUT_DQ) times the OLTF to estimate the optical gain noise ASD (this last step was a quick attempt at budgeting the calibration noise).
1. For each ASD we used N = 24 averages, from which we estimate rms (statistical) uncertainties which are depicted by error bands ( $\pm \sigma$ ) around the lines.

** Note: We ran the same procedure using dtt (diaggui) to validate our estimates at every point, as well as check our SNR in b and d before taking the ~3.5 hours of data.

16369

Thu Sep 30 18:04:31 2021

XARM OLTF (calibration) with three lines

[anchal, paco]

We repeated the same procedure as before, but with 3 different lines at 55.511, 154.11, and 1071.11 Hz. We overlay the OLTF magnitudes and phases with our latest model (which we have updated with Koji's help) and include the rms uncertainties as errorbars in Attachment #1.

We also plot the noise ASDs of calibrated OLTF magnitudes at the line frequencies in Attachment #2. These curves are created by calculating power spectral density of timeseries of OLTF values at the line frequencies generated by demodulated XARM_IN and ETMX_LSC_OUT signals. We have overlayed the TRX noise spectrum here as an attempt to see if we can budget the noise measured in values of G to the fluctuation in optical gain due to changing power in the arms. We multiplied the the transmission ASD with the value of OLTF at those frequencies as the transfger function from normalized optical gain to the total transfer function value.

It is weird that the fluctuations in transmission power at 1 mHz always crosses the total noise in the OLTF value in all calibration lines. This could be an artificat of our data analysis though.

Even if the contribution of the fluctuating power is correct, there is remaining excess noise in the OLTF to be budgeted.

16373

Mon Oct 4 15:50:31 2021

Hang

Fisher matrix estimation on XARM parameters

[Anchal, Hang]

What: Anchal and I measured the XARM OLTF last Thursday.

Goal: 1. measure the 2 zeros and 2 poles in the analog whitening filter, and potentially constrain the cavity pole and an overall gain.

2. Compare the parameter distribution obtained from measurements and that estimated analytically from the Fisher matrix calculation.

3. Obtain the optimized excitation spectrum for future measurements.

How: we inject at C1:SUS-ETMX_LSC_EXC so that each digital count should be directly proportional to the force applied to the suspension. We read out the signal at C1:SUS-ETMX_LSC_OUT_DQ. We use an approximately white excitation in the 50-300 Hz band, and intentionally choose the coherence to be only slightly above 0.9 so that we can get some statistical error to be compared with the Fisher matrix's prediction. For each measurement, we use a bandwidth of 0.25 Hz and 10 averages (no overlapping between adjacent segments).

The 2 zeros and 2 poles in the analog whitening filter and an overall gain are treated as free parameters to be fitted, while the rest are taken from the model by Anchal and Paco (elog:16363). The optical response of the arm cavity seems missing in that model, and thus we additionally include a real pole (for the cavity pole) in the model we fit. Thus in total, our model has 6 free parameters, 2 zeros, 3 poles, and 1 overall gain.

The analysis codes are pushed to the 40m/sysID repo.

===========================================================

Results:

Fig. 1 shows one measurement. The gray trace is the data and the olive one is the maximum likelihood estimation. The uncertainty for each frequency bin is shown in the shaded region. Note that the SNR is related to the coherence as

SNR^2 = [coherence / (1-coherence)] * (# of average),

and for a complex TF written as G = A * exp[1j*Phi], one can show the uncertainty is given by

\Delta A / A = 1/SNR, \Delta \Phi = 1/SNR [rad].

Fig. 2. The gray contours show the 1- and 2-sigma levels of the model parameters using the Fisher matrix calculation. We repeated the measurement shown in Fig. 1 three times, and the best-fit parameters for each measurement are indicated in the red-crosses. Although we only did a small number of experiments, the amount of scattering is consistent with the Fisher matrix's prediction, giving us some confidence in our analytical calculation.

One thing to note though is that in order to fit the measured data, we would need an additional pole at around 1,500 Hz. This seems a bit low for the cavity pole frequency. For aLIGO w/ 4km arms, the single-arm pole is about 40-50 Hz. The arm is 100 times shorter here and I would naively expect the cavity pole to be at 3k-4k Hz if the test masses are similar.

Fig. 3. We then follow the algorithm outlined in Pintelon & Schoukens, sec. 5.4.2.2, to calculate how we should change the excitation spectrum. Note that here we are fixing the rms of the force applied to the suspension constant.

Fig. 4 then shows how the expected error changes as we optimize the excitation. It seems in this case a white-ish excitation is already decent (as the TF itself is quite flat in the range of interest), and we only get some mild improvement as we iterate the excitation spectra (note we use the color gray, olive, and purple for the results after the 0th, 1st, and 2nd iteration; same color-coding as in Fig. 3).

16399

Wed Oct 13 15:36:38 2021

We did a few quick XARM oltf measurements. We excited C1:LSC-ETMX_EXC with a broadband white noise upto 4 kHz. The timestamps for the measurements are: 1318199043 (start) - 1318199427 (end).

We will process the measurement to compute the cavity pole and analog filter poles & zeros later.

16957

Tue Jun 28 17:07:47 2022

Added Beatnote channels in demodulation of c1cal

I added today demodulation of C1:LSC-BEATX/Y_FINE_I/Q in the c1cal demodulation where different degrees of freedom can be dithered. For McCal (formerly soCal), we'll dither the arm cavity for which we can use any of the DOFs (like DARM) to send the dither to ETMX/ETMY. Then with green laser locked as well, we'll get the calibration signal from the beatnotes in the demodulaed channels. We can also read right after the mixing in c1cal model and try differnt poles for integration .

I've also added medm screens in the sensing matrix part of LSC screen. These let you see demodulation of beatnote frequency signals.

17010

Mon Jul 18 04:42:54 2022

Error propagation to astrophysical parameters from detector calibration uncertainty

We can calculate how much detector calibration uncertainty affects the estimation of astrophysical parameters using the following method:

Let $\overrightarrow{\Theta}$ be set of astrophysical parameters (like component masses, distance etc), $\overrightarrow{\Lambda}$ be set of detector parameters (like detector pole, gain or simply transfer function vaue for each frequency bin). If true GW waveform is given by $h(f; \overrightarrow{\Theta})$ , and the detector transfer function is given by $\mathcal{R}(f; \overrightarrow{\Lambda})$ , then the detected gravitational waveform becomes:
$g(f; \Theta, \Lambda) = \frac{\mathcal{R}(f; \overrightarrow{\Lambda_t})}{\mathcal{R}(f; \overrightarrow{\Lambda})} h(f; \overrightarrow{\Theta})$

One can calculate a derivative of waveform with respect to the different parameters and calculate Fisher matrix as (see correction in 40m/17017):

$\Gamma_{ij} = \left( \frac{\partial g}{\partial \mu_i} | \frac{\partial g}{\partial \mu_j}\right )$

where the bracket denotes iner product defined as:

$\left( k_1 | k_2 \right) = 4 Re \left( \int df \frac{k_1(f)^* k_2(f))}{S_{det}(f)}\right)$

where $S_{det}(f)$ is strain noise PSD of the detector.

With the gamma matrix in hand, the error propagation from detector parameter fractional errors $\frac{\Delta \Lambda_j}{\Lambda_j}$ to astrophysical paramter fractional errors $\frac{\Delta \Theta_i}{\Theta_i}$ is given by (eq 26 in Evan et al 2019 Class. Quantum Grav. 36 205006):

$\frac{\Delta \Theta_j}{\Theta_j} = - \mathbf{H}^{-1} \mathbf{M} \frac{\Delta \Lambda_j}{\Lambda_j}$

where $\mathbf{H}_{ij} = \left( \frac{\partial g}{\partial \Theta_i} | \frac{\partial g}{\partial \Theta_j}\right )$ and $\mathbf{M}_{ij} = \left( \frac{\partial g}{\partial \Lambda_i} | \frac{\partial g}{\partial \Theta_j}\right )$ .

Using the above mentioned formalism, I looked into two ways of calculating error propagation from detector calibration error to astrophysical paramter estimations:

Using detector response function model:

If we assume detector response function as a simple DC gain (4.2 W/nm) and one pole (500 Hz) transfer function, we can plot conversion of pole frequency error into astrophysical parameter errors. I took two cases:

Binary Neutron Star merger with star masses of 1.3 and 1.35 solar masses at 100 Mpc distance with a $\tilde{\Lambda}$ of 500. (Attachment 1)
Binary black hole merger with black masses of 35 and 30 at 400 MPc distance with spin along z direction of 0.5 and 0.8. (I do not fully understand the meaning of these spin components but a pycbc waveform generation model still lets me calculate the effect of detector errors) (Attachment 2)

The plots are plotted in both loglog and linear plots to show the order of magnitude effect and how the error propsagation slope is different for different parameters. 'm still not sure which way is the best to convey the information. The way to read this plot is for a given error say 4% in pole frequency determination, what is the expected error in component masses, merger distance etc. I

Note that the overall gain of detector response is not sensitive to astrophysical error estimation.

Using detector transfer function as frequency bin wise multi-parameter function

Alternatively, we can choose to not fit any model to the detector transfer function and simply use the errors in magnitude and phase at each frequency point as an independent parameter in the above formalism. This then lets us see what is the error propagation slope for each frequency point. The hope is to identify which parts of the calibration function are more important to calibrate with low uncertainty to have the least effect on astrophysical parameter estimation. Attachment 3 and 4 show these plots for BNS and BBH cases mentioned above. The top panel is the error propagation slope at each frequency due to error in magnitude of the detector transfer function at that frequency and the bottom panel is the error propagation slope at each frequency due to error in phase of the detector transfer function.

The calibration error in magnitude and phase as a function of frequency would be multiplied by the curves and summed together, to get total uncertainty in each parameter estimation.

This is my first attempt at this problem, so I expect to have made some mistakes. Please let me know if you can point out any. Like, do the order of magnitude and shape of error propagation makes sense? Also, comments/suggestions on the inference of these plots would be helpful.

Finally, I haven't yet tried seeing how these curves change for different true values of the merger event parameters. I'm not yet sure what is the best way to extract some general information for a variety of merger parameters.

Future goals are to utilize this information in informing system identification method i.e. multicolor calibration scheme parameters like calibration line frequencies and strength.

Code location

17011

Mon Jul 18 15:17:51 2022

Hang

Error propagation to astrophysical parameters from detector calibration uncertainty

1. In the error propogation equation, it should be \Delta \Theta = -H^{-1} M \Delta \Lambda, instead of the fractional error.

2. For the astro parameters, in general you would need t_c for the time of coalescence and \phi_c for the phase. See, e.g., https://ui.adsabs.harvard.edu/abs/1994PhRvD..49.2658C/abstract.

3. Fig. 1 looks very nice to me, yet I don't understand Fig. 3... Why would phase or amplitude uncertainties at 30 Hz affect the tidal deformability? The tide should be visible only > 500 Hz.

4. For BBH, we don't measure individual spin well but only their mass-weighted sum, \chi_eff = (m_1*a_1 + m_2*a_2)/(m_1 + m_2). If you treat S1z and S2z as free parameters, your matrix is likely degenerate. Might want to double-check. Also, for a BBH, you don't need to extend the signal much higher than \omega ~ 0.4/M_tot ~ 10^4 Hz * (Ms/M_tot). So if the total mass is ~ 100 Ms, then the highest frequency should be ~ 100 Hz. Above this number there is no signal.

17017

Tue Jul 19 07:34:46 2022

Error propagation to astrophysical parameters from detector calibration uncertainty

Addressing the comments as numbered:

Yeah, that's correct, that equation normally $\Delta \Theta = -\mathbf{H}^{-1} \mathbf{M} \Delta \Lambda$ but it is different if I define $\Gamma$ bit differently that I did in the code, correct my definition of $\Gamma$ to :
$\Gamma_{ij} = \mu_i \mu_j \left( \frac{\partial g}{\partial \mu_i} | \frac{\partial g}{\partial \mu_j} \right )$
then the relation between fractional errors of detector parameter and astrophysical parameters is:
$\frac{\Delta \Theta}{\Theta} = - \mathbf{H}^{-1} \mathbf{M} \frac{\Delta \Lambda}{\Lambda}$
I prefer this as the relation between fractional errors is a dimensionless way to see it.
Thanks for pointing this out. I didn't see these parameters used anywhere in the examples (in fact there is no t_c in documentation even though it works). Using these did not affect the shape of error propagation slope function vs frequency but reduced the slope for chirped Mass by a couple of order of magnitudes.
1. I used the get_t_merger(f_gw, M1, M2) function from Hang's work to calculate t_c by assuming $f_{gw}$ must be the lowest frequency that comes within the detection band during inspiral. This function is:
  $t_c = \frac{5}{256 \pi^{8/3}} \left(\frac{c^3}{G M_c}\right)^{5/3} f_{gw}^{-8/3}$
  For my calculations, I've taken $f_{gw}$ as 20 Hz.
2. I used the get_f_gw_2(f_gw_1, M1, M2, t) function from Hang's work to calculate the evolution of the frequency of the IMR defined as:
  $f_{gw}(t) = \left( f_{gw0}^{-8/3} - \frac{768}{15} \pi^{8/3} \left(\frac{G M_c}{c^3}\right)^{5/3} t \right)^{-3/8}$
  where $f_{gw0}$ is the frequency at t=0. I integrated this frequency evolution for t_c time to get the coalescence phase phi_c as:
  $\phi_c = \int^{t_c}_0 2 \pi f_{gw}(t) dt$
In Fig 1, which representation makes more sense, loglog of linear axis plot? Regarding the affect of uncertainties on Tidal amplitude below 500 Hz, I agree that I was also expecting more contribution from higher frequencies. I did find one bug in my code that I corrected but it did not affect this point. Maybe the SNR of chosen BNS parameters (which is ~28) is too low for tidal information to come reliably anyways and the curve is just an inverse of the strain noise PSD, that is all the information is dumped below statistical noise. Maybe someone else can also take a look at get_fisher2() function that I wrote to do this calculation.
Now, I have made BBH parameters such that the spin of the two black holes would be assumed the same along z. You were right, the gamma matrix was degenerate before. To your second point, I think the curve also shows that above ~200 Hz, there is not much contribution to the uncertainty of any parameter, and it rolls-off very steeply. I've reduced the yspan of the plot to see the details of the curve in the relevant region.

Quote:

1. In the error propogation equation, it should be \Delta \Theta = -H^{-1} M \Delta \Lambda, instead of the fractional error.

2. For the astro parameters, in general you would need t_c for the time of coalescence and \phi_c for the phase. See, e.g., https://ui.adsabs.harvard.edu/abs/1994PhRvD..49.2658C/abstract.

3. Fig. 1 looks very nice to me, yet I don't understand Fig. 3... Why would phase or amplitude uncertainties at 30 Hz affect the tidal deformability? The tide should be visible only > 500 Hz.

17029

Sun Jul 24 08:56:01 2022

Hang

Error propagation to astrophysical parameters from detector calibration uncertainty

Sorry I forgot to put tc & phic in the example.

I modified astroFisherLib.py to include these parameters. Please note that their meaning is that we don't know when the signal happens and at which phase it merges.

It does not mean the time & phase from a reference frequency to the merger. This part is not free to vary because it is fixed by the intrinsic parameters.

It might be good to have a quick scan through the Cutler & Flanagan 94 paper to better understand their physical meanings.

17221

Wed Nov 2 16:34:56 2022

Single arm calibration run

[Anchal, Paco]

We added a notch filter on ETMY (the actuation point of the YARM control loop) to inject our calibration line at 575.170 Hz. The excitation is injected using the DARM Oscillator, with an exc. gain of ~ 500 (this gets us a decent > 10 SNR line in the ALS Y beat). With the arm cavity locked to the PSL (~150 Hz control bandwidth), and the aux laser locked to the cavity (~10 kHz control bandwidth) the goal of this run is to calibrate our actuator strength and more importantly to budget its uncertainty. For this we have looked at the ALS beat stability using Allan statistics; we noticed the ALS beatnote frequency fluctuations start to become dominated by 1/f (or divergent noise due to systematic drifts in the YARM loop) after 10 seconds (see Attachment #1) (we have managed to see 30 seconds stability with the HEPAs off and without locking to IMC).

Our prediction is that our demodulated calibration lines will display the least residual rms noise when averaging down to around this time. This is the only reason one would use allan statistics; to quantify the separation between statistical and systematic effects in a frequency measurement. To be continued...

gpstimes:

1351464997 to 1351467139
1351467259 to 1351468221
1351468318 to ...

17253

Thu Nov 10 17:40:31 2022

Plan to calibrate single arm actuation strength

Lock single arm cavity (e.g. YARM)
Lock YAUX laser to arm cavity (actuation point is ETMY)
With the notch on the YARM loop filter (actually on ETMY),
Turn on cal line (e.g. DARM osc) to move ITMY; here the frequency is chosen to be away from 600 Hz (line harmonic) and from violin modes for ITMY (642 Hz). The lower value of 575.17 Hz was chosen to avoid demodulating noise peaks at 455 Hz and 700 Hz.
Get raw YALS beatnote (we chose the demod angle of -35 deg to minimize Q).

The analysis is as follows:

Get demodulated IQ timeseries for the duration of the locks before lowpass filter (C1:CAL-SENSMAT_DARM_BEATYF_I_DEMOD_I_IN1); we are also storing the raw beatnote if we want to do software demodulation.
Look at the allan deviation of I and Q to establish the timescale over which our measurement is dominated by statistical uncertainty -- after this time, the uncertainty is expected to be due to systematic error / drift. In this case as shown by Attachment #1 the time is around 60.6 seconds.
At this frequency and with 500 gain the ITMY coils should be actuating 7.32 pm of amplitude displacement.
The minimum allan deviation does indeed predict the statistical uncertainty limited rms if we look at the power spectra of the demodulated cal line over different time periods (Attachment #2), notice I lowpassed the raw timeseries.
I think the next step is to get the nominal calibration value and repeat the measurment for more than a single cal line.
Roughly from the deviation plot, our fractional beatnote deviation is a proxy for the calibration uncertainty. 1.15e-16 of beatnote stability should translate to a fractional displacement stability of ~4.57e-15 at 60 seconds; giving an ultimate statistical calibration uncertainty of 0.06% at this particular frequency when averaging for this long. It might be interesting to see a calibration frequency dependent allan deviation plot.

17257

Fri Nov 11 14:15:45 2022

I turned all the LSC oscillators on and used the digital demod for BEATYF (fine y als beat) to grab the data. For this I added notches onto SCY ETMY LSC filter banks FM6-10 to account for these lines at 30.92, 211.10, 313.31, 315.17 Hz (basically just reusing the osc models) and adjusted the sensing matrix to actuate on ITMY.

I aligned and locked YARM, and then I aligned and locked the YAUX. The lock seems pretty robust with an avg green transmission GTRY ~ 0.185 counts for TEM00.

Trying to see other lines appear on the BEATYF demod channels, but so far no luck. I scaled down the exc gain from 500 counts (snr ~ 20 at 575 Hz) and verified the notches are working. Since I am unsure of the issue here and WFS tests are happening at 4 today, I decided to take some beat data under different conditions -->

HEPA OFF and PSL Shutter Open

gpstime start = 1352244763

gpstime end = 1352245405

HEPA OFF and

PSL Shutter Closed

gpstime start = 1352245574

gpstime end = 1352246216

HEPA ON and

PSL Shutter Closed

gpstime start = 1352246240

gpstime end = 1352246882

17266

Tue Nov 15 11:04:01 2022

YALS Hardware inspection

The ALS Cal for ITMY actuation was off by ~ 1000, so I decided I don't trust / understand what this beatnote is seeing. Then, I went in the lab to inspect things;

ALS DFD and DEMOD: The demod box was off perhaps on purpose, more likely by accident... the switch was off in the rear of the 1U box in the LSC rack... Also, the DB9 cable labeled ALS was disconnected. Fixed both these bugs and verified it worked all the way into cds.
YARM Green injection: Did some re-alignment, and noted the mode was hopping a bit too much (once every couple of seconds) so I rotated the half waveplate before the green PZT steering mirrors by ~ 8 deg and used the latter to get a GTRY transmission of > 0.320 (counts), about 75% more than last time. Finally I made sure the mode is robustly locked for several minutes.

Beatnote calibration

The factor above may be explained by the bogus signals coming into the beat fine phase channels on the ALS model. After locking the YARM with POY11, and locking the YAUX to the YARM cavity, I turned on the LSC oscillators -- all five of them see Attachment #1 for the screenshot -- and looked for the lines in the C1:ALS-BEATY_FINE_PHASE_OUT channel. Here, again the sensing output matrix was set up to actuate on ITMY, while the ETMY (control point of YARM loop) had all the output notches on. Once all lines were visible in the YAUX beatnote, I had to reduce the LSC filter gain from 0.012 to 0.011 to prevent loop oscillations... Then I recorded the gpstimes below with different conditions.

HEPA ON (JC Inside lab)
- gpstime = 1352575798 to 1352576046
HEPA OFF (JC Inside lab)
- gpstime = 1352576125 to 1352576216
HEPA OFF (JC in the control room)
- gpstime = 1352576641 to 1352577591

Analysis

Basically, only the DARM line was recorded (DQ channs) so I modified the c1cal to store the SIG_OUT and DEMOD_I_IN1 channels for both BEATX and BEATY cal signals. This means I need to repeat this measurement. In the meantime I am also going to try and rerun calibrate the BEAT HZ transfer function.

17291

Mon Nov 21 11:52:50 2022

Radhika

[Paco, Radhika]

We set out to realign the YARM AUX laser input into the arm cavity.

- We noticed that the GTRY beam was way off the center of the screen, so we went to the vertex table to align the camera.

- The beam spot at GTRY PD was large/divergent, so we shifted the PD closer to the penultimate mirror. We also doubled the PD gain. Transmission went from ~0.3 to ~0.7 (with gain doubled).

- We returned to the YARM end table to finalize alignment with the green PZT steering mirrors. GTRY was maximized to ~0.77.

17328

Wed Nov 30 20:01:08 2022

rana

I don't think you need to record the excitations. They are just sine waves. The amplitude you can read off from the OSC screen. You just have to have the BEAT channel recorded and you can demod it to get the calibration. If the BEAT channel is calibrated in Hz, and you know the 40m arm length, then you're all done.

Quote:

Analysis

17329

Thu Dec 1 20:43:25 2022

[Anchal, Paco]

We are doing this attempt again in following configuration:

PSL shutter is closed. (So IR laser is free running)
Beanote frequency between Y arm and Main laser is about 45 MHz.
Green laser on Y end is locked. Transmission is above 1.1 (C1:ALS-TRY_OUT)
All calibration oscillators are turned on and set to actuate ITMY. See screenshot attached.
The calibration model was changed to demodulate the C1:ALS-BEATY_FINE_PHASE_OUT channel insteald. We'll have DQ channels before mixing with oscillator, after mixing, and also after applying a 4th order 30 Hz butterworth filter.

Start time:

PST: 2022-12-01 20:44:23.982114 PST
UTC: 2022-12-02 04:44:23.982114 UTC
GPS: 1353991481.982114

Stop time:

PST: 2022-12-02 14:32:29.547603 PST
UTC: 2022-12-02 22:32:29.547603 UTC
GPS: 1354055567.547603

17346

Thu Dec 8 16:21:40 2022

ITMY actuation strength cal with 5 lines

[Anchal, Paco]

After debugging the hardware, on gpstime 1354422834 we turned on 5 cal lines on ITMY to test the ALS calibration for the single arm along with our error estimates.

Note: the YARM IR lock lasted > 8 hours, but the GRY transmission dropped twice during the evening and hopped back up, so the phase tracker jumped a couple of times.

Configuration

YAUX laser was locked to the YARM through the analog PDH servo (UGF ~ 2 kHz), YARM was locked to the PSL with POY11 (UGF ~ 200 Hz), and the ALS phase tracker was set to output the beat frequency noise in Hz. HEPA was left on during this measurement. The oscillators were similar to previous instances: gains of 70@211.1Hz, 100@313.31Hz, 100@315.17Hz, 300@575.17Hz and 15@30.92Hz with appropriate notches on ETMY to avoid POY11 loop supression.

Analysis

For YARM, the high bandwidth YAUX laser loop with transfer function G ensures that the relative laser frequency fluctuations correlate with the relative length fluctuations as:

$\frac{\delta \nu}{\nu} = \frac{G}{1 - G} {\frac{\delta L}{L}}$

Then, getting the magnitude of the YARM displacement at calibration frequencies is possible by knowing the arm cavity length, open loop gain, and absolute frequency (wavelength). The relative calibration error on the magnitude of the displacement is

$\frac{\Delta \lvert \delta L\lvert}{\lvert \delta L \lvert} = \left[ \left(\frac{\Delta {L}}{L}\right)^2+ \left(\frac{\Delta {\lambda}}{\lambda}\right)^2 + \left(\frac{\Delta {{\delta \nu}}}{{\delta \nu}}\right)^2 + \left(\frac{\Delta \lvert G \lvert}{\lvert G \lvert(\lvert G \lvert - 1)}\right)^2 \right]^{1/2}$

including the relative uncertainties in the YARM length, wavelength, and open loop gain. Interestingly, the loop gain term weighs proportionally less as G increases, so even if G = 100 (10), its relative error contribution would be < 1%. To estimate our total error, we assume the wavelength and YARM length are 1064.1(5) and 37.79(1), and add the frequency dependent values for G with 10% error. Finally, we use the rms ASD to estimate the relative error from the beatnote fluctuation measurement.

The measurement was done similar to other instances, taking the 'C1:ALS-BEATY_FINE_PHASE_OUT_HZ_DQ' timeseries (sampled at 16 kHz) and demodulating at the calibration frequencies above to get the mean YAUX laser frequency fluctuation and its uncertainty from the demodulated rms ASD.

Results

Attachment #1 shows the raw timeseries, Attachment #2 shows the spectra around the cal lines, Attachment #3 shows the demodulated timeseries, Attachment #4 shows the final result for the 5 lines, including the tallied errors as detailed above.

ITMY actuation = 4.92(11) nm / count / f^2

Discussion

We compared our results from Attachment #4 against a MICH referenced ITMY actuation calibration found here; which Yuta guess-timated a 10% uncertainty (gray shaded band in Attachment #4). An important correction came for the 575 Hz line, not just because the YAUX OLG is small but because a violin filter on ITMY LSC output has a 1.4475 gain bump. In fact we collected any additional digital gains from the ITMY output filters:

Output filter digital gains at cal lines (from foton)
30.92 Hz	211.1 Hz	313.31 Hz	315.31 Hz	575.17 Hz
1.00007	1.0034	1.0101	1.01017	1.4475

Consider moving the 575 Hz line to avoid additional digital gain, but try to remain at high frequency.
- Maybe here we can use the resonant gain MOKU filters that Radhika is designing.
Setup a live loop gain calibration to reduce the uncertainty for the high frequency cal line.
- We can also just grab GTRY (transmission) as a proxy for optical gain and use for budgeting.
Work on setting up constraints for error mitigation based on allan deviation of the beatnote and PDH nonlinearity.
Move this to FPMI or some other lock configuration

17374

Fri Dec 30 11:38:45 2022

ALS Calibration errors -- single arm actuation

Here are my thoughts on calibration errors. This applies to the single arm actuation calibration, but may easily be extended to calibrate the DARM residual noise for example.

According to the math in this post, there are four parameters whose estimates build the total calibration uncertainty: arm length, wavelength, loop gain, and beatnote fluctuation. Below is a table for how each is measured, what the sources of statistical versus systematic error are, how large each relative contribution roughly is, and how we may improve on them.

	Arm Length	Wavelength	ALS beat fluctuation	AUX Loop gain
Current Estimate	FSR scan using ALS, reference to POX/POY (11 MHz) sideband.	NPRO Specification	DFD + Phase tracker	Swept sine
Statistical error	Limited by scan range (e.g. DFD range) and resolution.	No statistical error from specification	Shot noise limited measurement ?	Bendat Piersol Table 9.6 (depends on coherence)
Systematic error	Limited by freq reference offsets (Marconi), and residual POS-PIT coupling	NPRO half tuning range	Flicker noise (electronics, other)	Swept sine bias and servo drift (thermal, flicker)
Improvements	More FSRs per scan at best possible resolution	Iodine spectrsocopy	Lower ALS residual frequency noise	Higher AUX servo gain

Arm length

Statistical

The arm length has been estimated before by locking the arm with the ALS beat, scanning the arm length and looking at the IR resonances. From the statistical uncertainty standpoint the limit seems to be number of measurable FSRs. Using these numbers, the statistical uncertainty comes to ~ 0.6%. Other attempts claim to have improved on this by almost an order of magnitude giving ~ 0.02%. Simply scanning over more FSRs should improve this as the usual square root number of measurements statistical error reduction.

Systematic

Systematic error comes from the frequency referenced on this measurement (the Marconi 11 MHz sideband oscillator), nonlinearities in the mostly linear scan (e.g. POS to PIT coupling). I think it's safe to neglect frequency offsets > 1 Hz because of the Rb clock reference and the Marconi's own calibration. I'm not sure about the POS to PIT coupling magnitude and whether F2A filters are helping here, but offset scan nonlinearities should distort the FSR nonuniformly and this error may have sneaked into the statistical estimate above. From the posts referenced above, the scan result seems extremely linear, but repeating the measurement and plotting the residuals may give an accurate estimate of the nonlinearity. I think either of the systematic errors discussed here should be below or around the ppm (0.0001%) level, but this should be confirmed.

Wavelength

Statistical

If we use the NPRO specification Mephisto's wavelength is 1064 nm and there is no statistical error.

If on the other hand we do iodine absorption spectroscopy, we may be able to see ~ 4 lines throughout the 30 GHz tuning range of the NPRO. Fun fact: 30 GHz correspond to 1 cm-1 (inverse centimeter). Assuming we can scan our laser with a 0.01 cm-1 resolution, it may be possible to estimate the absolute wavelength to 10 better than any line center. The ATLAS of iodine spectroscopy gives strong absorption lines around 532 nm to 0.001 cm-1, or 0.00001% accuracy. A simple Doppler broadened absorption should improve this further.

Systematic

If we use the NPRO specification the systematic error comes from the number of known significant figures ~ 0.047 %. A slightly better estimate comes from the Prometheus model with a frequency doubled output hitting near the P 83(33-0) line of iodine, at 18787.814 cm-1. This gives a nominal wavelength of 532.259 nm, or 1064.518 nm on the seed. Because the tuning range is 30 GHz, our systematic error may be +0.03 nm - 0.07 nm from 1064.52 nm. Taking the median of 0.05 nm, the relative systematic error from the Nd:YAG specification is 46.9 ppm = 0.0047%.

If on the other hand we do iodine spectroscopy, the systematic error will be dominated by the residual shifts on the iodine vapor, which are negligibly small compared to the Doppler broadened lines. They might add sub-ppm uncertainty to the absolute calibration.

Beat fluctuations

Statistical

The error in estimating beatnote fluctuations is statistically dominated if our beat detection is shot noise limited for example. Other stationary noises with power law spectra decaying faster than 1/f are subject to this effect. The allan deviation discriminates the timescales at which our measurement is dominated by statistical error. Currently our calibration lines have SNR of 10 to 100. Averaging seems to be limited to ~ 100 second timescales, so the statistical error on these measurements is ~ 0.1 to 1.0 %.

Systematic

As suggested in the above, the allan deviation discriminates the statistical dominated timescales for this measurement. There is a correspondence between noise spectra and allan deviation, so we should be able to point out what noises contribute to systematic drift in our ALS noise budget.

Loop gain

Statistical

Invoking Bendat and Piersol, Table 9.6 gives the statistical estimate of loop gain estimate with coherence gamma, and n_avg number of averages. Because of our resonant OL gain filters, assuming G ~ 100 there, and high coherence on the OLTF measurement so gamma ~ 0.9, with 12 averages we should get a relative loop gain error of 9.8%.

Systematic

Also from B&P, we should estimate how biased our TF is. This depends on delays, bandwidths, measurement device noises, etc. Furthermore, the analog electronics in the AUX servo should drift, but we neglect this contribution for now. A simple bias estimate (eq 9.75) tells us that the coherence is biased by the number of averages such that in our estimate above the unbiased coherence is roughly 0.897. This means our systematic contribution from TF bias is ~ 0.17 %.

We should remember that in the case of loop gain, the total error (systematic + statistical) gets an extra suppression factor of the order of the loop gain itself. Radhika's resonant digital gain filters should easily allocate 40 dB (or ~ 100) on every calibration line such that our total calibration error drops to the 0.1% level.

Conclusions

The main calibration error (at the 1% level) seems to be from the ALS beat frequency. We can simply crank the SNR up, but we should work on the ALS relative stability. I think the low frequency end of the ALS residual frequency noise is currently limited by DFD... I wonder if we can improve on this by implementing a digital PLL (e.g. using a Moku)

17376

Sat Dec 31 19:27:32 2022

ETMY actuation strength cal with 5 lines

Calibrated ETMY actuation strength using ALS. Attachment #1 shows the result, in close agreement with this previous measurement and a combined uncertainty estimate of 0.88%. The data was taken with 5 oscillators on, at slightly different strengths than with the ITMY run, and the actuation was on ETMY.

The ETMY actuation strength is

10.843e-9 / f^2 +- (0.88)% [m/count]

Note: gpstimes for raw data are 1356490036 to 1356495400 (a little bit over an hour and a half).

17377

Sat Dec 31 20:00:03 2022

rana

ETMY actuation strength cal with 5 lines

how much frequency dependent variation in the transfer function do we expect? Are there resonances in the mechanical suspension cage or the violin modes?
it would be interesting to make the same TF, but from counts to coil current, and see if the variation is there. I don't expect to see much variation in the electronics, but there might be something. In principle, we can just measure the voltage at the satellite box, but the coil cable's capacitance and the coil inductance make for some wiggle. I think the resonant frequency ought to be ~ 100 kHz, but perhaps there are some wiggles due to frequency response in the AI filter or something.

17384

Wed Jan 4 12:12:57 2023

ALS calibration error from DFD

[Paco, Anchal]

One of the crucial and currently limiting calibration errors is in the ALS beat. We think a major driver of calibration uncertainty may be the DFD TF calibration since it depends on the RF beat power.

The RF beat power as monitored by C1:ALS-BEATY_RF_POW shows relative fluctuations of 0.02% at the 16 Hz sampling rate (actually 0.09% rms from Attachment #1) once the single arm and YAUX laser are both locked. This sounds ok, but that's just a statistical estimate. The beat frequency changes every time we break and reacquire the lock, making the BEAT PD frequency response and DFD overall calibration change their nominal TF. This changes can be significantly larger than 0.02% (e.g 4.9% = observed change in the RF power after breaking and reacquiring the YAUX lock this morning). This is far more significant than the contribution from the RIN on the two lasers.

This kind of systematic error could be addressed by either/all:

Stabilizing the ALS BEAT absolute frequency (offset locking using freq counter and simple integrator or equivalent)
Rejecting RF beat amplitude fluctuations by mixing a stable DC voltage in the DFD, or using a comparator.
- Our plan this afternoon is to quickly measure the RF AM rejection level from a simple mixer and a stable voltage reference and plan ahead.
ISS on both lasers

17477

Wed Feb 22 23:40:48 2023

Alex

Adding calibration constants for sus matrix and filter control buttons to the sus control screen

The callibration constants were updated for the oplev pitch and yaw. The values were changed as denoted in 17471 were:

PITH: 175.7→ 155 cts/urad

YAW: 193 → 241 cts/urad

To make these changes for the oplev callibration constants I went to ETMY - SELECTED OPLEV SERVO BOX

I then opened OLMATRIX and turned off PITCH and YAW servos in the ETMY SUSPENSION SCREEN such that the system does not attempt to actively make corrections while values are being changed.

Then I adjusted the matrix to include our updated calibration constants and reinitiated the oplev ptich and yaw servo's

This updated the calibration constants for everything

The next change that was made was the addition of the calibration filters for position, pitch, yaw and side into the sitemap view for the suspension systems.

Adding calibration filters will allow us to callibrate the pos, pitch, yaw, and side to true values of urad and umeters (see 17459)

The final screen may be seen bellow (the updated area is outlined in red):

When each of the filter buttons is clicked, the following screen will now appear (circled in yellow is the calibration constant gain we will be calculating and entering into the system):

To create the edits to the controls screen we must complete the following process

We can edit the original screen - right click > evaluate > edit this screen

Then I adjusted the width of the overall screen, and moved the right half of the modules over to the right so I could fit in some filter buttons. I then Navigated to the c1ioo wfs master screen using the open feature to copy a pre existing filter module

I then adjusted the filter module and its contents to correspond to the features and autogenerated model files from RTCDS

There was some rearranging and adjusting needed to get these files in place first. The autogenerated files from the RTCDS can be found in dir = "/opt/rtcds/caltech/c1/medm/c1sus/"

They were autogenerated with names "C1SUS_BS_PITCAL.adl", "C1SUS_BS_POSCAL.adl", "C1SUS_BS_YAWCAL.adl","C1SUS_BS_SIDECAL.adl"

We copied these files to dir = "/opt/rtcds/userapps/trunk/sus/c1/medm/templates/NEW_SUS_SCREENS/"

The file names were changed to "SUS_PITCAL.adl", "SUS_POSCAL.adl", "SUS_YAWCAL.adl", "SUS_SIDECAL.adl"

The directory we placed them in is where the models for c1 sus can be found that are referenced by the sitemap suspension monitor screen

Each file was then opened in Vscode and a few changes were made such that the specific naming values referenced by the different screens of the sitemap and different optics, are replaced by the overarching values seen in each instance of the screens.

There are approximately 50 referenced file names of "C1:SUS-BS_PITCAL" etc. In each instance we made the following changes:

"-BS" was changed to "-$(OPTIC)"

"C1:" was changed to "$(IFO):"

The new strings should read "$(IFO):SUS-$(OPTIC)_PITCAL"

Once this change was made we can now right click on the filter module box, click on "Label/Name/Args" button

In the display file, we must add the path name for the calibration directory "/opt/rtcds/userapps/trunk/sus/c1/medm/templates/NEW_SUS_SCREENS/SUS_POSCAL.adl"

And for the arguments box we will enter OPTIC=$(OPTIC), IFO=$(IFO)

You can also copy and paste the directory names in the file boxes using right click copy from the file manager and paste into the box using a single click of the mouse scroller wheel

Lastly, the PV limits were changed for each number output right click value box > PV limits > Precision > Source changed to "Default" with a value of 1.

The shown value of the position, pitch, yaw, and side was then changed to show the output from the newly added filter. This is done also by right clicking the value box and adjusting the "Readback Channel".

Value changed from "$(IFO):SUS-$(OPTIC)_TO_COIL_1_#_INMON" to the outputs from the filters which are

"$(IFO):SUS-$(OPTIC)_POSCAL_OUTMON" (for others changing POSCAL to the appropriate variable)

This is how to edit and add the Medm screens for single suspension optics into the sitemap IFO SUS screen

Lastly, Tomohiro and I worked on acquiring 6 data sets from DC stepping through adjustments in pitch and yaw for MC1, MC2 and MC3. These datasets will be fit quadratically and combined with more tests dine by AC driving the stepper motors tomorrow to find the calibration constants for the mirrors.

17492

Sat Mar 4 18:57:18 2023

Configuration

FPMI DARM calibration run

Locked FPMI, measured DARM and CARM OLTFs, locked YAUX and measured the analog loop TF at the cal line frequencies. Turned the cal lines on with the new filters Anchal added on MC2 (ResGain within and Notches outside the CARM bandwidth which is set to 200 Hz), and hope to get 3600 seconds of data this evening. Log and measurement data are saved under /opt/rtcds/caltech/c1/Git/40m/scripts/CAL/FPMI

17499

Wed Mar 8 18:32:22 2023

Configuration

FPMI DARM calibration run set to happen at 1 am

On rossa in tmux session name FPMI_DARM_Cal, a script is running to take FPMI DARM calibration data at 1:00 am on March 9th. Please do not disturb the experiment untill 6 am. To stop the script do following on rossa:

tmux a -t FPMI_DARM_Cal

ctrl-C

The script will lock both arms, run ASS, then lock FPMI, then tune beatnote frequency with Y AUX laser to around 40 MHz, set phase tracked UGF to 2 kHz, clear phase history, take OLTF of DARM from 2 kHz to 10 Hz, take OLTF of CARM and AUX loop at calibration line frequencies, turn on the calibration lines, and wait for FPMI to unlock or 5 hours to pass, whatever happens first. At the end it will turn off the calibration lines.

17502

Thu Mar 9 19:20:44 2023

Configuration

FPMI DARM calibration run set to happen at 1 am

Running this test again tonight. Will probably run it every night now.

17603

Thu May 25 14:40:03 2023

Measured the PSL wavelength

The PSL wavelength is 1064.5068 +- 0.0015 nm

[Paco, Yehonathan]

With yehonathan's help, we borrowed a WS6-200 wavemeter and calibrated the PSL wavelength using the frequency doubled beam at the PSL table. For this we gathered a FC/APC to FC/PC fiber, cleaned both ends, and launched ~ 6 uW of power into the wavemeter (Attachment #1 shows the setup). The vacuum wavelength was measured to be 532.2534 nm, (see picture in Attachment #2) implying a PSL wavelength of 1064.5068 nm. This is not too far from my previous estimate! The wavemeter claims a "200 MHz accuracy" so we used this as a standard error to estimate the uncertainty of 2 * 0.7 pm @ 532 nm = 1.5 pm @ 1064 nm. This leaves us with 1.4 ppm of relative wavelength error in the ALS based calibration.

It should be noted that the Nd:YAG temperature was set to 30.615(5) degrees (?) and the injection current to 2.100 Amps for this measurement.

17618

Sat Jun 3 19:56:59 2023

ITMY calibration with ALS and swept line

ITMY actuator calibrated with higher resolution using ALS

Today I came briefly in the lab and restarted all models because the beams were missing in the BHD camera; implying the ASS model controlling TTs has tripped into a weird state. The problem got fixed after that and I recovered the IFO alignment more or less. Thus I began another quick calibration run --

I wrote a script to put calibration line and update the LSC YARM notch filter using python-foton so as to take an ALS calibration sweep of the YARM test mass actuator responses. For a given sweep frequency array, the script loops over the frequency, updates the notchSensMat (LSC-YARM FM10), loads the coefficients, and ramps up the SENSMAT OSC gain and frequency to inject the noise. The YAUX laser is locked to the YARM and YARM is locked to PSL during this measurement. Then, the real-time demodulated BEAT_Y signals are averaged for ~ 3 seconds (just wanted a quick scan, not a very accurate or precise one) and the result is plotted in Attachment #1. Notice that I have included the phase tracker loop correction to account for the high frequency calibration line inferred response (1 + i f/2000)

I then used foton in gui mode and exported the violin filter transfer function (FM1-4 and FM6 for ITMY) magnitude and inverted its effect to get the "dc actuation strength" of ITMY.

Discussion

The value is consistent with our recently poor actuation due to c1sus - DAC-0 failure, at ~ 3 nm /count /f^2. I look forward to another measurement after the DAC-0 has been replaced / fixed.
No more "high frequency" lift as seen before?
There is still some residual frequency dependence near the phase tracker BW; maybe not just a simple integrator?

The idea of having the response shape is so I can then use the 5 lines only to get the response to high accuracy.

17621

Mon Jun 5 18:42:13 2023

ITMY calibration with ALS and swept line

ITMY actuator calibrated with higher resolution using ALS after c1sus-DAC0 replacement

I repeated the previous measurement (with fewer points) and the result is summarized in Attachment #1 -- just as a quick confirmation that the actuation strength (POS) is restored to its nominal ~ 5 nm / ct / f^2 value.

(plot updated Tue Jun 6 10:34:27 2023 without violin filters and higher resolution)

17624

Wed Jun 7 15:51:47 2023

Koji