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• To learn how to run the analysis scripts which we think are in /cds/data/psdm/xpp/xppx49520/scratch/ffb/smalldata_tools/
• Yanwen writes: "an example will be run 622, experiment xppx49520. most runs in xppx49520 are usable."
• Another similar expt xpplx9221 in s3df
• look at smd droplet code in ARP
• line 278
• 
• get_droplet_params: old psana is in ADU, RMS is 3 (0.15keV) use 5 times that for threshold
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• don't need the precise geometry of the four detectors
• 4 epix100 detectors
• used to read ADUs but now psana does keV.  threshold in keV is ~9.9?
• XPCS data
• put the detector is very far back
• each detector covers a very small solid-angle, so all pixels about "the same"
  • sometimes you have zoom in to an ROI so all pixels look the same
• threshold is critical
• bad pixels done by psana.  mask is used to get rid of high-intensity regions
• each pixel should show up equally: if a pixel "stands out" with too many hits mask it out as a hot pixel
• also need to mask out cosmic rays, and radiation background from trace elements concrete (at higher energies).  could leave to a second offline stage
• pixels with connecting borders form a droplet.  don't use scipy.label, not sure why.
• there is a fifth detector, but has too many photons?
• from droplet, assign a number of photons
• use "greedy guess" for assigning photons
• different algorithms have different biases, have to "calibrate" the bias
  • two main ways to calibration:
    • find a speckle pattern with known contrast, use unfocused beam (100s micron, vs usual 1 micron).  Use that to measure bias.
    • second way cannot be done per-frame.  measuring a change on picosecond timescale.  measure a sequence of speckle patterns: not related.  turns out adding two subsequent frames (long timescale) halves the "contrast-beta".  Have to find two frames of similar intensity to add.  Need to add them together before the photonization.  can't data-reduce them.
    • bias changes as function of temperature but other than that it's pretty constant: a characteristic of the algorithm and the detector.  depends on how the charge-cloud size compares to the pixel size of the detector
    • also have simulated data where ground truth is known
  • would like to label calibration runs like dark runs
• everything up until now everything is more generally interesting: not just XPCS 
• goal: get the contrast Beta from the ratio of 1-photon and 2-photon droplets
  • some corrections from the pulse-to-pulse intensity using I0 measurement (e.g. SASI pulse intensity)
  • can defer I0 correction to offline (not drp)
• photon occupancy is 10^-4 (per pixel) for XPCS.  XES is larger.  Also need 2-photon events to get beta.
  • droplet might be enough (don't need photonizing?)
  • need the location of each pixel
  • save i,j,intensity (don't need the droplet-label, can be computed from i,j)
• get one number for a contrast (beta) compare to different samples under different conditions.
  • need 0.5 million frames (~ 1 hour of data taking 
• beta is -0.038 +- .007.
• want to see a "trend" in beta as a function of tau (separation of two pulses)
  • can also look as a function of Q
  • get tau from accelerator: doesn't vary shot to shot or from path-length change of a mirror.
• watch for count-rate dependence
  • bin according to different intensity and measure beta

Two main tasks:

• implement two-threshold dropletizing algorithm for drp (including data-format for I,j,intensity for a multi-panel detector)
  • gpu's could still help with the thresholding in drp
  • need to calibrate the data
• how much gpu/cpu resources do we need to do 4Mpx 25kHz offline analysis with the above reduced data?
  • includes photonizing, rejection of cosmic rays and other background radiation
  • code is "loopy" python, Silke has version with less loops
  • greedy guess algorithm assumes photon is in 2 pixels (like Chuck's idea for 2 pixel photons)
    • a little different in how it handles the last small pieces of photons 
    • avoids time-consuming gaussian fits: maybe neural nets could do this faster?
  • could consider neural net

Meeting with Diling 2021

Nov. 12, 2021 and Nov. 18, 2021

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