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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/
Full python implementation here: https://github.com/slac-lcls/smalldata_tools/blob/master/smalldata_tools/ana_funcs/droplet.py and https://github.com/slac-lcls/smalldata_tools/blob/master/smalldata_tools/ana_funcs/droplet2Photons.py - 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
- 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
- two main ways to calibration:
- 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
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