HAWKI: Detector monitoring
QC1 database (advanced users):
Trending of detector monitoring is based on measurement of the QC1
parameters of Detector Linearity calibration data. They are obtained
with a dedicated Health Check OB executed approximately once a monthly. The
Detector Linearity OB consits of 10 sets of data - each set is one
DARK and two Imaging FLAT FIELD frames taken with the same DIT. The
DIT is gradulally increased from set to set to increase the flat flux
level. The data are obtained with the Ks filter using the Nasmyth shutter as
a source: its flux is then dependent on the ambient temperature as
expected from the Planck law.
As of 2016-09-28 only the effective linearity is
monitored. The conversion factor (conad/gain) measurements were found
unreliable. The corresponding HC plot was taken off-line untill
template and/or pipeline recipe are improved.
The monitored effective nonlinearity is a
difference at 10 000 ADU between the flux obtained
from the 2nd order polynomial fit of flux
vs. exposure time and the flux predicted by the
linear term only, normalized by the polynomial flux.
|QC1 database: table, name
| [docuSys coming]
|*Class: KPI - instrument performance; HC - instrument health; CAL - calibration quality; ENG - engineering parameter
**There might be more than one.
The effective linearity is trended for each detector
separately. Also, value averaged across all 4 chips
and its standard deviation are monitored.
Scoring&thresholds Effective linearity
In general, the effective linearity is relatively
stable. Outliers are likely to be due to variations in
the flux level as there is no constant source of
| mid-September 2014
| indication of problem with non-linearity in all chips (followed in PPRS-05910); it was fixed and data taken on 2014-10-10 scored "green" again
| August 2015
| all parameters nominal after GRAAL installation
| Analysis of the detector monitoring data sets showed that the gain measurements cannot give reliable estimates. The QC_GAIN monitoring HC plot is taken off-line untill template and/or pipeline recipe is improved.
Algorithm Effective linearity
The effective non-linearity is the difference between the
polynomial flux and the one predicted by the linear fit at
user-defined flux level, normalized with the polynomial flux