3.3	Structure of calacs

The calacs package consists of four tasks listed in Table 3.2. These tasks, available in the stsdas.hst_calib.acs package, are called automatically by calacs, but each may be run separately should some variation in the normal processing be desired by the user.

Table 3.2: Tasks in the calacs Pipeline

acsccd	CCD specific calibrations
acsrej	Cosmic ray rejection task
acs2d	Basic MAMA and CCD calibrations
acssum	Repeated observations summing task for MAMA data

The flow of data through the ACS calibration pipeline and the decisions made while working with associated data are diagrammed in Figures 3.1, 3.2, and 3.3. They’re also outlined below with the calacs tasks and functions in parenthesis.

•	calculate a noise model for each pixel and record it in the error (ERR) array. (acsccd/doNoise or acs2d/doNoise)

•	Flag known bad pixels and saturated pixels in the data quality (DQ) array. (acsccd/doDQI or acs2d/doDQI)

•	Subtract the bias level, which is determined from overscan regions (CCD only). (acsccd/doBlev)

•	Subtract the bias image (CCD only). (acsccd/doBias)

•	Subtract the post-flash image, if required (CCD only). (acsccd/doFlash)

•	Perform cosmic ray rejection and combining of images obtained by “CR-SPLIT” and repeated exposures1 (CCD only). (acsrej)

•	Perform global linearity corrections (MAMA only). (acs2d/doNonLin)

•	Scale and subtract the dark image and calculate the mean dark value (CCD only). (acs2d/doDark)

•	Divide the image by the flat field, then multiply by the gain. (acs2d/doFlat)

•	Calculate photometry header keywords for flux conversion (except for slitless spectroscopy modes). (acs2d/doPhot)

•	Calculate image statistics; these values are stored in the Archive Database and calibrated data headers. (acs2d/doStat)

•	Sum repeated subexposures in an exposure. (acssum)

 

Calibrated flt.fits images from “CR-SPLIT” exposures, repeated sub-exposures, “POS TARG” exposures, or dither “PATTERN” exposures are combined using multidrizzle as follows:

•	for “POS TARG” or dithered data, compute offsets between images based on the image headers’ WCS1 ( this is done using pydrizzle).

•	Create bad pixel and cosmic ray masks.

•	Drizzle input flt.fits images into a combined output image with suffix drz.fits, using bad pixel and cosmic ray masks, while also applying image offsets and performing geometric distortion corrections. The resulting image units is in electrons/second.

As indicated in Figure 3.1, calibration tasks that are detector-specific (e.g., acsccd for WFC data only) have been separated from tasks that can process both detectors (e.g., acs2d). For example, MAMA data obtained with the SBC does not have overscan regions like that in CCD data.

The initial processing performed on CCD data alone is shown in Figure 3.2. Reference files appropriate for each processing step and the calibration switches controlling them are also given beside the name of the task they control. The output (overscan-trimmed image) from acsccd is then sent through acs2d as shown in Figure 3.3.

Processing of raw MAMA data begins with acs2d, which initializes the error and data quality arrays (a step that was performed earlier for CCD data) and applies linearity corrections.

Figure 3.1: Flow Diagram for ACS Data, with calacs Task Names

CRCORR Combine observations to reject cosmic rays.

RPTCORR Add individual repeat observations (SBC MAMA only).

DRIZCORR Drizzle processing.

Figure 3.2: Flow Diagram for CCD Data Using acsccd in calacs

* The overscan areas are trimmed only if the raw image had “BLEVCORR=PERFORM.” In almost all cases, this is the default value. Only in rare instances will that keyword be set to “OMIT.”

Figure 3.3: Flow Diagram for MAMA and Overscan-trimmed CCD Data using acs2d in calacs

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1

The World Coordinate System (WCS) convention defines keywords and usage that provide the description of astronomical coordinate systems in a FITS image header.