The filters were somewhat successful, how much we can see in attachment 1. The tip about difference between eigenmode basis and cartesian basis was the main thing that helped us take data properly. We still used OSEM data but rotated the output from POS, PIT, YAW to x, theta, phi (cartesian basis where x is also measured as angle projected by suspension length).

Eigenmode basis and Cartesian basis:

• It is important to understand the difference between these two and what channels/sensors read what.
• Eigenmode basis as the name suggests is the natural basis for the suspended pendulum.
  • It signifies the motion along three independent and orthogonal modes of motion: POS (longitudinal pendulum oscillation), PIT, and YAW.
  • The position of optic can be written in eigenmode basis as three numbers:
    • POS: Angle made by the center of mass of optic with verticle line from suspension point.
    • PIT: Angle made by the optic face with the suspension wires (this is important to note).
    • YAW: Angle made by optic surface with the nominal plane of suspension wires. (the yaw angle basically).
• Cartesian basis is the lab reference frame.
  • Here we define three variables that can also represent an optic positioned and orientation:
    • x: Angle made by the center of mass of optic with verticle line from suspension point. (Same as POS)
    • : Angle made by the optic surface with absolute verticle (z-axis) in lab frame.
    • : Twist of the optic around the z-axis. Same as YAW angle above.
• We want to apply the feedback gains and filters in eigenmode basis because they are a set of known independent modes. (RXA: NOOO!!!!!! read me elog entry on this topic)
• Hence, the output from input matrix of suspensions comes out at POS, PIT and YAW in the eigenmode basis.
• However, the sensors of optic positional, and orientation such at MC_F, wave front sensors and optical levers measure it in lab frame and thus in cartesian basis.
• Essentially, the measured by these sensors is different from the PIT calculated using the OSEM sensor data and is related by:
  • , where PIT and POS both are in radians as defined above.
• When we optimized the cross-coupling in output matrix at high frequencies using the MC_F and WFS data, we actually optimized it In cartesian basis.
• The three feedback filters from POS, PIT and YAW which carry data in the eigenmode basis need to be rotated into the cartesian basis in the output matrix before application to the coils.
• The so-called F2A and A2L filters are essentially doing this rotation.
• Above the resonant frequencies, the PIT and become identical. Hence we want our filters to go to unity

The two filter sets:

• The filters are named Eg2Ctv1 and Eg2Ctv2 on the POS column of MC2 output matrix.
• This is to signify that these filters convert the POS, PIT, and YAW basis data (eigenmode basis data) into the cartesian basis (x, theta, phi) in which the output matrix is already optimized at higher frequencies.
• v1 filter used an ideal output matrix during the calculation of filter as described in 16042 (script at scripts/SUS/OutMatCalc/coilBalanceDC.py).
• Attachment 2 shows these filter transfer functions.
• v2 filter use the output matrix optimized to reduce cross-coupling amount cartesian basis modes (MC_F, WFS_PIT and WFS_YAW) in 16009.
• Attachment 3 shows these filter transfer funcitons.
• Because of this, the v2 filter is different among right and left coils as well. We do see in Attachment 1 that this version of filter helps in reducing POS->YAW coupling too.

Test procedure:

• We uploaded both the diagonalized input matrix and the diagonalized output matrix as calculated earlier.
• We measured channels C1:SUS-MC2_SUSPOS_IN1_DQ, C1:SUS-MC2_SUSPIT_IN1_DQ, and C1:SUS-MC2_SUSYAW_IN1_DQ throughout this test.
• These channels give output in an eigenmode basis (POS, PIT, and YAW) and the rows of the input matrix have some arbitrary normalization.
• We normalize these channels to have same input matrix normalization as would be for ideal matrix (2 in each row).
• Then, assuming the UL_SENS, UR_SENS, LR_SENS, and LL_SENS channels that come at input of the input matrix are calibrated in units of um, we calculate the cartesian angles x, theta, phi. for this calculation, we used the distance between coils as 49.4 mm (got it from Koji) and length of suspension as 0.2489 m and offset of suspension points from COM, b = 0.9 mm.
• Now that we have true measures of angles in cartesian basis, we can use them to understand the effect on cross coupling from the filters we used.
• PSL shutter is closed and autolocker is disabled. During all data measurements, we switched of suspension damping loops. This would ensure that our low frequency excitation survives for measurement at the measurement channels.
• We first took reference data with no excitation and no filters for getting a baseline on each channel (dotted curves in Attachment 1).
• We then send excitation of 0.03 Hz with 500 counts amplitude at C1:SUS-MC2_LSC_EXC and switched on LSC output.
• One set of data is taken with no filters active (dashed curve in attachment 1).
• Then two sets of data are taken with the two filters. Each data set was of 500s in length.
• Welch function is used to take the PSD of data with bin widht of  0.01Hz and 9 averages.

Results:

• Filter v1 was the most successful in reducing  coupling by factor of 17.5.
  • The reduction in  coupling was less. By a factor of 1.4.
• Filter v2 was worse but still did a reduction of coupling by factor of 7.8.
  • The reduction in  coupling was better. By a factor of 3.3.

Next, filters in PIT columns too

• We do have filters calculated for PIT as well.
• Now that we know how to test these properly, we can test them tomorrow fairly quickly.
• For the YAW column though, the filters would probably just undo the output matrix optimization as they are derived from ideal transfer function models and ideally there is no coupling between YAW and other DOFs. So maybe, we should skip putting these on.