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For example, pure water ring background from exp=cxij4716:run=22 for single event:

To evaluate background in data, n-d array of data is split for 2-d bins in polar coordinate frame, total intensity and number of involved pixels are counted for each bin and converted to the average bin intensity.

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For good statistical precision of the background averaging 2-d bins should contain large number of pixels. However large bins produces significant binning artifacts which are seen in resulting image.
The main reason for angular bins is a variation of intensity with angle due to polarization effect. The beam polarization effect can be eliminated with appropriate correction.

Method for polarization correction factor:

Code Block

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def polarization_factor(rad, phi_deg, z) :
    """Returns per-pixel polarization factors, assuming that detector is perpendicular to Z.
    """
    phi = np.deg2rad(phi_deg)
    ones = np.ones_like(rad)
    theta = np.arctan2(rad, z)
    polsxc = 1 - np.sqrt(np.fabs(np.sin(theta)*np.cos(phi)))
    pol = 1 - sxc*sxc
    return divide_protected(ones, pol, vsub_zero=0)

Then, radial background can be estimated for a single angular bins (ring-shaped radial bins) :

Code Block
pf = polarization_factor(rb.pixel_rad(), rb.pixel_phi(), z) res = rb.subtract_bkgd(nda * pf)

For z=1m we get polarization correction factor, corrected data (water-ring) sample, and background subtracted data as follows.

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Effect of polarization is somehow accounted, but most likely the sample-to-detector distance 1m is not correct.

The data set exp=cxij4716:run=22 was collected at sample-to-detector distance z=94mm. In this case polarization correction formula gives distribution for correction factor and "corrected" data averaged over all 14636 events:

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So, it looks like polarization correction formula is wrong

CSPAD "dopping" artifacts

or several angular bins:

Code Block

arr = load_txt(fname_nda)
rb = RadialBkgd(X, Y, mask, nradbins=500, nphibins=1)
pf = gu.reshape_to_2d(polarization_factor(rb.pixel_rad(), rb.pixel_phi(), 0.5e694e3))
nda = rb.subtract_bkgd(arr * pf) * mask.flatten()

For exp=cxij4716:run=22 z=94mm

averaged over all 14636 events calibrated (pedestal, common mode)

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data
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polarization correction factor
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polarization corrected data
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radial-background subtracted data using single angular bin
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radial-background subtracted data using 8 angular bins
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