TES Readout Implementation

\This page provides an overview of the status and inner workings of the TES Readout development and eventual implementation.

Description of TES

TESMap.pdf

PseudoCode

This is a proposed algorithm to handle the data produced by the TES detectors for LCLS-II.

The data is assumed to consist of several time streams per FPGA (250) sampled at 1 MHz. The beam repetition rate is 10 kHz, for a total of 100 samples minimum between valid (photon generated) events. The samples are assumed to be 16 bits.

Parfor : Parallel For

NumberOfChannels : number of channels to be handles by the FPGA

Samples per Window : Number of samples in a single pulse window

TotalNumberOfWindows : Maximum number of windows to be kept in memory for the configured filters. Currently only 3 for a total of four cases. The active window, the preceding one and the following one. Possibly more windows to be added before/after for more precision in the energy measurement.

Active Window : Window on which we are performing the filter calculation

Current Window : Window that is currently loading in buffers.

EventMatrix : Matrix containgin information about the presence of events in each window of each channel.

CreateCircularBuffers (NumberOfChannels,
SamplesPerWindow, TotalNumberOfWindows)
LoadFilters()
ConfigureDataChannel()

While IncomingData:
            LoadDatainBuffers()
            ApplyCrosstalkCorrection(All_Channels,CurrentWindow)

            EventMatrix = TagEvents(All_Channels)

	For each ActiveWindow : 
            WindowIncrement=+1
				Parfor each Channel:            
                	Switch (CheckEvents(EventMatrix)):
						Case No Event Before And After
						Energy = ApplyFilter(Filter00)
						Case Event Before And After
						Energy = ApplyFilter(Filter11)

						Case Event Before Only
						Energy = ApplyFilter(Filter10)

						Case Event After Only
						Energy = ApplyFilter(Filter00)


            SaveEnergyAndTimestamp(xtcFile)


		If WindowIncrement == SaveIncrement
			SaveWaveformtoXTC(xtcFile, TotalTimeWindow, AllChannels)


 


Def TagEvents:
	Parfor each channel : 
		If Slope > MinimumSlope:
			CurrentWindowEvent=True
		Else:
			CurrentWindowEvent=False
	
		EventMatrix(channel, currentWindowIndex) = CurrentWindowEvent
	Return EventMatrix

Def ApplyFilter(Filter):
	For each i, sample :
		Temp = linearFunction(sample) * sample 
		Temp = temp* Filter(i) *Filter2(i) %More filters added if necessary
		Energy = sum(temp)
		%Details on operations required for this step
		% Can we pipeline the linear function with the multiply and add?

Benchmarks

This table lists the latest benchmarks.

Currently, it evaluates the average time (over 1e6 iterations) to complete the number of products indicated as well as the sum of the resulting vector. The length of the vector varies according to the number of samples.

The code currently uses a simple decision tree which directly indicates how many filters to use. Once we know how many filters are necessary this will become a fixed value. Meanwhile a new decision  tree will determine the case to use depending on which windows have a hit, currently in progress.

These benchmarks were obtained os psanagpu116, using O3 and vectored optimization.

g++ -std=c++11 -O3 -DNDEBUG -march=native -IEigen main.cpp

All values are in μs.

References

Ullom and Bennet. Review of superconducting transition sensors for X-ray and gamma-ray spectroscopy

Irwin and Hilton. Transition-edge sensors.

Tasks