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Message ID: 13294     Entry time: Tue Sep 5 16:37:47 2017
Author: Gabriele 
Type: Summary 
Category: LSC 
Subject: Improved PRMI deep learning reconstruction 

This is an update on the results already presented earlier (refer to elog 13274 for more introductory details). I improved significantly the results with the following tricks:

  • I retuned the demodulation phase of AS55, this time ensuring that the (alleged) MICH motion is visible mostly in Q when crossing a carrier resonance. Further fine tunings of phases will be possible once we have a measurement of the length optical matrix

  • I fine tuned the netwrok by training it again using the real data. The ides is the following. I started with the network trained on the simulated data, and froze the parameters of the input recurrent layers. I fed the real signal to the network, computed the reconstructed PRCL/MICH, and fed them to my PRMI model to compute simulated signals. I allowed some of the parameters of the models to vary (expecially demodulation phases). I then trained again the network by trying to match the model predicted signals with the real input signals. I allowed only the parameters of the fully connected layers to vary (mostly for technical reasons, I'm working on re-training also the recurrent layers)

An example of time domain reconstruction is visible below. It already looks better than the old results:

As before, to better evaluate the performance I plotted averaged values of the real signals as a function of the reconstructed MICH and PRCL positions. The results are compared with simulation below. They match quite well (left real data, right simualtion expectation)

One thing to better understand is that MICH seems to be somewhat compressed: most of the output values are between -100 and +100 nm, instead of the expected -lambda/4, lambda/4. The reason is still unclear to me. It might be a bug that I haven't been able to track down yet.

ELOG V3.1.3-