ESPRESSO: LED flat fields
|
HC PLOTS |
Bad pixels |
|
CONAD |
|
QC1 database (advanced users):
browse |
plot
|
With LED flat-fields, the spectrograph is not within the lightpath and the detector is completely illuminated. LED FFs are used to determine
the number of bad pixels and the detector gain (conversion factor CONAD). They
are measured on a regular basis for all read-out modes and detector binnings (1x1, 2x1, 4x2, and 8x4). A complete set consists of flat-fields with at least three different
exposure times and at least five frames per exposure time.
|
Raw LED FF. Example frame for the blue detector. The gaps are due to the pre-/overscan of the 16 read-out ports
|
Bad pixels
QC1_parameters
FITS key |
QC1 database: table, name |
definition |
class* |
HC_plot** |
more docu |
none | espresso_ledff..badpix_nb_tot | total number of bad pixels per detector | HC | | [docuSys coming] |
none | espresso_ledff..badpix_nb_rms | RMS bad pixel number across read-out ports | HC | | [docuSys coming] |
*Class: KPI - instrument performance; HC - instrument health; CAL - calibration quality; ENG - engineering parameter
**There might be more than one. |
ESPRESSO has two chips, one for the BLUE and one for the RED arm. Each chip
has 16 read-out ports. LED-FF QC parameters are determined by the pipeline per read-out port which
leads to 16 different values for each QC parameter per chip. They are written
as header keys in the form of (for the bad pixel number):
QC.EXT<n>.ROX<x>.ROY<y>.BADPIX.NB
with <n> being the detector (0 for BLUE and 1 for RED),
<x> the x position of the
read-out on the chip (0, 1, to 7), and <Y> the y position (0 or 1). The
corresponding names in the QC1 database follow the scheme
badpix_nb_<x>_<y> .
In addition to these individual values, the total number, the average, and the RMS per chip
are calculated and written into the QC1 database.
Trending
The number of bad pixels is trended separately for the four different read-out
modes: 1x1 binning (fast read-out), and 2x1, 4x2, and 8x4 binning (all slow
read-out).
Scoring&thresholds Bad pixels
The total number of bad pixels is scored per detector.
Thresholds have been set according to the measured values during commissioning.
History
None.
Algorithm Bad pixels
Bad pixels are detected based on their non-linear behaviour when dividing
frames with different exposure times.
The pipeline recipe determines the QC parameters per read-out port. The QC procedures calculate in addition the total number, the average, and the RMS
of all 16 ports per detector.
Conversion factor
QC1_parameters
FITS key |
QC1 database: table, name |
definition |
class* |
HC_plot** |
more docu |
none | espresso_ledff..gain_avg | average gain across read-out ports [e-/ADU] | HC | | [docuSys coming] |
none | espresso_ledff..gain_rms | RMS of gain numbers across read-out ports [e-/ADU] | HC | | [docuSys coming] |
*Class: KPI - instrument performance; HC - instrument health; CAL - calibration quality; ENG - engineering parameter
**There might be more than one. |
ESPRESSO has two chips, one for the BLUE and one for the RED arm. Each chip
has 16 read-out ports. LED-FF QC parameters are determined by the pipeline per read-out port which
leads to 16 different values for each QC parameter per chip. They are written
as header keys in the form of (for the CONAD):
QC.EXT<n>.ROX<x>.ROY<y>.CONAD
with <n> being the detector (0 for BLUE and 1 for RED),
<x> the x position of the
read-out on the chip (0, 1, to 7), and <Y> the y position (0 or 1). The
corresponding names in the QC1 database follow the scheme
gain_<x>_<y> .
In addition to these individual values, the average and the RMS per chip
are calculated and written into the QC1 database.
Trending
CONAD is trended separately for the four different read-out
modes: 1x1 binning (fast read-out), and 2x1, 4x2, and 8x4 binning (all slow
read-out).
Scoring&thresholds Conversion factor
Scoring thresholds have been set close to the nominal value of 1.1 e-/ADU.
History
First pipeline versions used "GAIN" instead of "CONAD" for the names of the header
keywords. The parameters in the QC1 database still have "gain" in their names;
this will be changed eventually.
Algorithm Conversion factor
CONAD gives the conversion from ADU into electrons in e-/ADU.
The mean and difference frames of two exposures with the same exposure time
are computed. Then, the standard deviation of the difference frame is
calculated. CONAD is measured from the relation between mean flux level and
standard deviation.
|