6. Processing description¶
This section gives a description of each step of the pipeline in a greater detail and list the parameters that can be changed if needed.
Next figure shows the main steps that are involved in the PANIC pipeline:
- the non-linearity is corrected;
- a master flat-field in computed by combining all the appropriate images without offsets; if the mosaic is of an Sky-Target type, only the sky frames are used;
- a bad pixel mask, is computed by detecting, in the master flat field, the pixels with deviant values;
- if provided, an external bad pixel mask is also used, adding the bad pixel in the previus one;
- for each image, a sky frame is computed by combining a certain number of the closest images;
- this sky frame is subtracted by the image and the result is divided by the master flat;
- bright objects are detected by SExtractor in these cleaned images to measure the offsets among the images; the object mask are multiplied by the bad pixel mask to remove false detections;
- a cross-correlation algorithm among the object masks is used to measure the relative offsets. It also works if no object is common to all the images;
- the cleaned images are combined using the offsets, creating the “quick” image;
- to remove the effect of faint obejcts on the estimate of the sky frames, SExtractor is used on the combined image to create a master object mask;
- the object mask is dilatated by a certain factor to remove also the undetected object tails;
- for each image a new sky is computed by taking into account this object mask;
- if field distortion can be neglected, these images are combined by using the old offsets, creating the “science” image;
- field distortion is removed from the cleaned images by using SCAMP computed distortion model
- the pixels containing deviant pixels are identified and flagged;
- the old offsets could be effected by field distortion, therefore new offsets are computed for the undistorted images;
- finally, the cleaned corrected images are combined.
6.1.2. Data-set classification¶
One of the main features of PAPI is that the software is able to do an automatic data reduction. While most of the pipelines are run interactively, PAPI is able to run without human interaction. It is done because of the classificaton algorithm that is implemented in PAPI and that allow an automatic identification of the data sets grouping the files according to the observation definition with the OT.
1 - The data grouping algorithm 2 - Sky finding algorithm for extended objects
In case of not using the OT during the observation, also a data grouping is possible, althouth with some limitations. Let’s see how it works:
6.1.3. Data Preparation¶
Firstly, each FITS file is linearity corrected if it was enabled in the configuration file (nonlinearity:apply). If integrations where done with repetitions >1 and saved as a cube with N-planes, then the FITS cube is collapsed doing a simple arithmetich sum of N-planes.
Then the image is divided into the number of chips in the FPA (which constitutes 4 chips in a mosaic). From this step on, the pipeline works on individual chips rather than whole images, thereby enhancing the speed and enabling us to do multi-chip processing on multi CPUs.
In next sections we describe the main calibration to be done by PAPI.
6.2. Computing the master dark¶
6.3. Computing the master flat-field¶
6.4. Computing the Bad Pixel Mask¶
The map of all bad pixels (hot, low QE) are derived from the non-linearity tests. However, also the nonlinearity analysis provides a list of non-correctable pixels, which always will be considered invalid.
So, currently there is no procedure in PAPI to compute the right bad pixel mask (BPM).
6.4.1. First pass sky subtraction¶
6.5. Sky model¶
6.5.1. Object detection¶
6.5.2. Offset computation¶
6.5.3. First pass coaddition¶
6.5.4. Master object mask¶
SExtractor is again used to find objects in this first-pass coadded image in order to mask then during next sky estimation. This time the parameters controlling the detection threshold should be set to have deeper detections and mask faint objects. The parameters involved nad ther default values are:
mask_minarear = 10 mask_thresh = 1.5
The resulting object mask is extended by a certain fraction to reject also the undetected object tails.
HAWAII-2RG near-IR detectors exhibit an inherent non-linear response. It is caused by the change of the applied reverse bias voltage due to the accumulation of generated charge. The effect increases with signal levels, so that the measured signal deviates stronger from the incident photon number at higher levels, and eventually levels out when the pixel well reaches saturation.
The common approach is to extrapolate the true signal Si(t) from measurements with low values, and fit it as a function of the measured data S(t) with a polynomial of order n:
For the correction, PAPI uses a master Non-Linearity FITS file that store the fit to be applied to the raw images. There is file for each readout mode. The filename is composed as:
The FITS file has a primary header with no data, and two data extensions for each detector. They are labeled LINMAX<i> and LINPOLY<i> with i=1...4 being the quadrant index, numbered similar to the scheme for MEF data files from GEIRS. Note that the indices do not necessarily correspond to SG hardware IDs, which are written in the header instead.
The extension LINMAX<i> is a 32bit float 2048x2048 data array containing the maximum correctable signal for each detector. Uncorrectable pixels have a NaN instead of a numerical value. The extension and LINPOLY<i> is a 32bit float 2048x2048x4 data cube containing the polynomial coefficients c[1...4] in reverse order. The first slice in the cube is c, the second c, etc.
The module used to correct the non-linearity is
correctNonLinearity.py; in adition
the non-linearity correction can be enable in the configuration file $PAPI_CONFIG setting
in the nonlinearity section the keyword apply = True.
HAWAII2 sensors with multiple parallel readout sections can show crosstalk in form of compact positive and negative ghost images whose amplitude varies between readout sections. PAPI has a optional de-crosstalk module that assumes that the amplitude is the same, therefore the correction will only partially remove the effect (if at all). If you know in advance that this will be a problem for your science case, then consider choosing different camera rotator angles for your observations.
The first effort at characterizing and removing the cross-talks made use of the “Medamp” technique. By this we mean isolating then subtracting what is common to all 32 amplifiers. This effectively seems to remove the edge and negative cross-talks which both affect all 32 amplifiers. But it does not remove the positive crosstalk. Note that the assumption is that the amplitude of the edge and negative cross-talks is the same ona ll 32 channels. We tried inconclusively to prove/disprove that assumption. If amplifier-dependant, the amplitude variations must be less than 10%.
We experimented doing the medamp at various stages of the processing and found the best results when removing the crosstalk as the very last step, after sky subtraction. Rigorously, it should actually be the very first step since crosstalk effects are produced in the very last stages of image generation.
The module used to correct the crosstalk is
dxtalk.py.py; in adition
the crosstalk correction can be enable in the configuration file $PAPI_CONFIG setting
in the general section the keyword remove_crosstalk = True.
6.5.7. Extended Objects¶
If your targets are really extended and/or very faint, then you should seriously consider observing blank SKY fields. They will be recognized and automatically used in the correct manner once identified by PAPI. No additional settings have to be made. You should check though that the images have correct header keys.