Recipe muse_lingain: Compute the gain and a model of the residual non-linearity for each detector quadrant

Data Structures

struct  muse_lingain_params_s
 Structure to hold the parameters of the muse_lingain recipe. More...

Typedefs

typedef struct
muse_lingain_params_s 
muse_lingain_params_t
 Structure to hold the parameters of the muse_lingain recipe.

Detailed Description

Author:
Ralf Palsa

The recipe uses the bias and flat field images of a detector monitoring exposure sequence to determine the detector gain in counts/ADU and to model the residual non-linearity for each of the four detector quadrants of all IFUs.

All measurements done by the recipe are done on the illuminated parts of the detector, i.e. on the slices. The location of the slices is taken from the given trace table, which is a mandatory input. Using the traces of the slices on the detector a set of measurement windows is placed along these traces. The data used for the determination of the gain and the residual non-linearity is the taken from these windows.

Bad pixels indicated by an, optionally, provided bad pixel table, or flagged during the preprocessing (bias subtraction) of the input data are excluded from the measurements.

Local measurements of the read-out-noise, the signal and the gain are calculated for each of the measurement windows. Using these measurements the gain for each detector quadrant is computed as the zero-order coefficient of a 1st order polynomial fitted to the binned gain measurements as a function of the signal level.

The residual non-linearity is modelled by a (high) order polynomial which is fitted to the fractional percentage deviation of the count rate from an expected constant count rate (the linear case) as function of the signal level.


Typedef Documentation

Structure to hold the parameters of the muse_lingain recipe.

This structure contains the parameters for the recipe that may be set on the command line, in the configuration, or through the environment.


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