Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add detection QE spread for outlier estimation #1139

Merged
merged 2 commits into from
Jul 18, 2023
Merged

Add detection QE spread for outlier estimation #1139

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
3592e08
Add detection QE spread for outlier estimation
FrancaCassol Jul 12, 2023
4092af3
Make qe_dispersion configurable
FrancaCassol Jul 17, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 10 additions & 3 deletions lstchain/calib/camera/calibration_calculator.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -48,6 +48,11 @@ class CalibrationCalculator(Component):
help='Excess noise factor squared: 1+ Var(gain)/Mean(Gain)**2'
).tag(config=True)

relative_qe_dispersion = traits.Float(
0.07,
help='Relative (effective) quantum efficiency dispersion of PMs over the camera'
).tag(config=True)

pedestal_product = traits.create_class_enum_trait(
PedestalCalculator,
default_value='PedestalIntegrator'
Expand Down Expand Up @@ -210,10 +215,12 @@ def calculate_calibration_coefficients(self, event):
# define unusables on number of estimated pe
npe_deviation = calib_data.n_pe - npe_median[:,np.newaxis]

# cut on the base of pe statistical uncertanty (neglect the 7% spread due to detection QE)
# cut on the base of pe statistical uncertainty (adding a 7% spread due to different detection QE among PMs)
tot_std = np.sqrt(npe_median + (self.relative_qe_dispersion * npe_median)**2)

npe_outliers = (
np.logical_or(npe_deviation < self.npe_median_cut_outliers[0] * np.sqrt(npe_median)[:,np.newaxis],
npe_deviation > self.npe_median_cut_outliers[1] * np.sqrt(npe_median)[:,np.newaxis]))
np.logical_or(npe_deviation < self.npe_median_cut_outliers[0] * tot_std[:,np.newaxis],
npe_deviation > self.npe_median_cut_outliers[1] * tot_std[:,np.newaxis]))

# calibration unusable pixels are an OR of all masks
calib_data.unusable_pixels = np.logical_or(unusable_pixels, npe_outliers)
Expand Down
Loading