1. X arm is totally misaligned in order to measure the Y arm loss using the reflection method. Each measurement round consists of measuring the reflected power when the Y arm is aligned and when it is misaligned.
2. The measurement script used is /scripts/lossmap_scripts/armLoss/measureArmLoss.py. It generates a log file in the /logs folder specifying the alignment and misalignment times.
3. The data extraction script dlData.py processes the raw data in the log file and creates a hdf5 file in the /Data folder conataining the data of the measurement it self.
4. dlData.py labels the the aligned and misaligned datas incorrectly when the number of measurement is odd. I use only even number of measurements then.
5. In order to clip the chaotic transition between the aligned and misaligned states I use tDur attribute smaller than the actual sleep time used in the measurement script itself.
6. plotData.py (written by Gautam) and AnalyzeLossData.ipynb (written by me) can be used to calculate the loss and to plot some analyses (see https://nodus.ligo.caltech.edu:8081/40m/14568). They give roughly the same answer. The descripancy can be explained by the different modulation and ITM transmissions used.
7. I take a measurement of 8 repeatitions. I plot the measured reflected power alternating between the aligned and misaligned states.
I find that the reflected power is repeatable to within 1%.
This is consistent with the transmission data plotted here which is also repeatable to within 1%.
8. I take an overnight measurement of 100 repeatitions.