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V3 Specifics

  • background shape is evaluated in run 162 WITHOUT common mode correction
  • pfv3r1 - is upgraded
  • S/N statistical significance is improved by increasing number of pixels

Data processing

exp=cxif5315:run=169

    nda_data = det.raw(evt)
    if nda_data is not None :
        nda =  np.array(nda_data, dtype=np.float32, copy=True)
        nda -= nda_peds
        nda = subtract_bkgd(nda, nda_bkgd, mask=nda_smask, winds=winds_bkgd, pbits=0)
        nda *= nda_smask

Peak finder parameters

alg_arc = PyAlgos(windows=winds_arc, mask=mask_arc, pbits=2)
alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=0, son_min=6)
#alg_arc.set_peak_selection_pars(npix_min=0, npix_max=1e6, amax_thr=0, atot_thr=500, son_min=6) # for v2r1

alg_equ = ... the same

        # in the event loop
        #peaks_arc = alg_arc.peak_finder_v2r1(nda, thr=30, r0=7, dr=2)
        #peaks_arc = alg_arc.peak_finder_v3r1(nda, rank=5, r0=7, dr=2)
        peaks_arc = alg_arc.peak_finder_v4r1(nda, thr_low=10, thr_high=150, rank=5, r0=7, dr=2)

Results

ARC

pfv2

 

 

 

 

 

 

pfv3

pfv4

 

 

EQU

 

 

 

 

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