Content

Analysis of cxif5315

New peakfinder

New peakfinder peak_finder_v2 from  class ImgAlgos.PyAlgos is used in this analysis with functionality as follows

• raw detector data is corrected for pedestals, background, and common mode,
• algorithm works in windows for good pixels defined in mask,

• each group of connected pixels above threshold thr are considered as a peak candidate, which parameters are evaluated and saved in the text file along with experiment, run, event time stamp information

  # Exp     Run  Date       Time      time(sec)   time(nsec) fiduc  Evnum  Reg  Seg  Row  Col  Npix      Amax      Atot   rcent   ccent rsigma  csigma rmin rmax cmin cmax    bkgd     rms     son  imrow   imcol     x[um]     y[um]     r[um]  phi[deg]

Example of peakfinder in the data processing script:

# initialization
winds_arc  = [ (s, 0, 185, 0, 388) for s in (0,1,7,8,9,15,16,17,23,24,25,31)]

alg_arc = PyAlgos(windows=winds_arc, mask=mask_arc, pbits=0)
alg_arc.set_peak_selection_pars(npix_min=5, npix_max=500, amax_thr=0, atot_thr=1000, son_min=6)
...

# in the event loop:
peaks_arc = alg_arc.peak_finder_v2(nda, thr=20, r0=5, dr=0.05)

#similar for EQUatorial region:
peaks_equ = alg_equ.peak_finder_v2(nda, thr=20, r0=5, dr=0.05)

Background subtraction algorithm

Averaged n-d array for (data) cxif5315-r0169 is used as a background. Normalization between data and background is done in four rectangular windows, highlighted in the plot:

from pyimgalgos.GlobalUtils import subtract_bkgd

# use part of segments 4 and 20 to subtr bkgd
winds_bkgd = [ (s, 10, 100, 270, 370) for s in (4,12,20,28)] 

        #calibration of raw data
        nda =  np.array(nda_raw, 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
        det.common_mode_apply(evt, nda)

Scripts

Data processing script uses direct access to data. Example is available since release ana-0.16.1 in

ImgAlgos/examples/ex_peakfinder_cspad.py

Results

Arc region

Peak data processing script is under development. Results for ARC region:

Plots for all peaks found by peakfinder in ARC region before selection:

Good peaks are selected as

def peakIsSelectedArc() :
    """Apply peak selection criteria to each peak from file
    """
    if sp.sonc<9    : return False
    if sp.atot<1800 : return False
    if sp.r<434     : return False
    if sp.r>442     : return False
    return True

where all threshold parameters were optimized to select maximum signal events at minimal background in two-peak distance distribution.

Plots for selected peaks

Single-peak events:

Two-peak events:

New peakfinder more than doubles the number of signal events in the distance distribution.

Equ region

Peak selection in equatorial region

def peakIsSelectedEqu() :
    """Apply peak selection criteria to each peak from file
    """
    if sp.sonc<9    : return False
    if sp.atot<1800 : return False
    if sp.r<100     : return False
    if sp.r>450     : return False
    return True

Event selection

def eventIsSelected() :
    """Apply selection criteria to entire event, based on list of peaks 
    """
    sp.event_is_selected = False
    if sp.count_arc_pks_sel  > 2 : return False
    if sp.count_equ_pks_sel  > 5 : return False
    #if sp.count_equ_pks_sel  < 0 : return False
    # Require all peaks in the narrow range of dphi[deg] 
    for pk in sp.lst_equ_evt_peaks :
        if math.fabs(pk.dphicmb) > 6 : return False
    sp.event_is_selected = True
    return True

Plots for raw peak data

Delta phi

delta phi for peaks around 0°, 180°, and combined distribution

Angles phi and beta

Evaluated for 2-peak events

Fitted for 2-peak events - these histograms are almost identical to evaluated

Fitted for >2-peak events

Fitted for ≥2-peak events

These fitted angles will be used for Fraser transformation for central part of distributions.

References

• class ImgAlgos.PyAlgos - collection of n-d array processing algorithms
• class Detector.PyDetector - direct access to detector data and calibration n-d arrays