One of the many perks of working with HST WFC3 data is that they can be downloaded from the Multimission Archive at Space Telescope (MAST) both in raw form and in calibrated form. If no special initial processing is needed, which is the case for our data, then they can be downloaded having already been through a standard image processing pipeline called “calwf3:. This pipeline treats the UVIS and IR data slightly differently, but in the end outputs images in the same stage of preliminary processing that carry the suffix “flt.fits”.
The basic UVIS pipeline processing begins with a module called “wf3ccd”, which creates a ERR (error) and DQ (data quality) fits extension by computing the error for each pixel and flagging known bad pixels. It then subtracts the bias and trims the overscan regions. Next, “wf3rej” is called to reject cosmic rays and combine the images from the two chips into one. Lastly, the images are run through “wf32d” to be dark-subtracted and flat-fielded. Because IR detectors are different from CCDs in that all the pixels are independently sampled and can be read out non-destructively, the IR pipeline is slightly different. The first module called “wf3ir” flags bad pixels, subtracts the zeroth read, attaches ERR and DQ extensions, corrects for non-linearity, subtracts the dark current (very important in the IR), and flat fields the images. Lastly, the data are sent through wf3rej, which is the
same as for the UVIS cosmic ray rejection. All of these steps can be done even before the data are downloaded from the MAST archive because they are standard steps that usually do not require customization. The next steps including aligning, correcting for geometric distortions, and stacking the images all require the user-modified input parameters, so the data are downloaded and run through another pipeline package called Drizzlepac.
Our first post-download step was to run the data through a task called TweakReg in order to very precisely align the images. Because the goal of our science is to identify the sames stars in images taken across several epochs, it is very important to align our images as accurately as possible. Though the headers of the fits files contain WCS information that can be used to align the images taken within the same visit, TweakReg is needed to account for slight pointing errors that occur between visits and correct these WCS files to within 0.1 pixel accuracy (4 mas for UVIS, 10 mas for IR) (Gilliland 2005). One cause of these pointing errors is that HST experiences periodic thermal expansion and contraction, or ‘breathing’, as it orbits the Earth. As the telescope breathes, the focus changes, thereby changing the shape and centroid location of the point spread function (PSF)1. This change in focus affects
the telescope’s Fine Guidance Sensors’ ability to track guide stars (see Drizzelpac Handbook Appendices for more info).
Another source of error that affects the alignment of images is geometric distortions. HST’s WFC3 images suffer from a few different sources of geometric distortions, the most obvious of which is tilt of the focal surface as it reaches the detectors. This is due to the limited amount of space each instrument’s optical bench can occupy and to the desire to
1A point spread function is the distribution of flux on the detector that is created by a point source of
reduce the number of reflections in the light path in order to preserve throughput. The UVIS detector is tilted approximately 21◦ with respect to one of its diagonals, whereas the IR detector is slanted at approximately 10◦ about its x-axis. This causes the UVIS field of view (FOV), when projected on the sky, to take the shape of a rhombus, and the IR FOV to resemble a rectangle. These tilts are not difficult to correct. What is difficult to correct is the change in plate scale across the UVIS and IR detectors. The slight change in plate scale leads to change in area-per-pixel of 7% across the diagonal of the UVIS chip and 4% across the IR chip.
To counteract breathing and geometric distortions to achieve sub-pixel image registration, TweakReg uses an algorithm similar to DAOFIND to identify point sources from each image. It then creates a catalog of source positions for each image and applies a distortion model that is stored in each image’s .fits header. Next, it matches these undistorted source positions to a user specified reference. In our case, we used the default reference image, which is the one that has the most sources in common with all the other images. To make sure cosmic rays or faint sources do not affect the alignment correction, tweakreg allows the user to specify a lower and upper pixel value threshold, which we chose 400 for our lower threshold and 50 000 for our maximum (below the full well depths of the IR and UVIS detectors). After all undistorted offsets between frames are calculated, they are used to update the WCS coordinates of each image’s header.
With the shifts stored in the image headers, the data were then stacked using the task Astrodrizzle. Astrodrizzle (short for astrometric drizzle) uses a technique known as drizzling (Fruchter & Hook 1997) formally referred to as variable-pixel linear reconstruction, the goal of which is to reconstruct the spatial information in a stacked image without altering the
signal-to-noise ratio. On a basic level, it works by combining two separate image reconstruc- tion methods into one: interlacing and shift and add. Interlacing, which really only works if dithers are integer pixel values away from one another, is just matching up de-shifted, corre- sponding pixels values and adding them together. The other method, shift-and-add, involves resampling the pixels onto a finer grid, then de-shifting them by the dither amount, and fi- nally adding them to the output image. This method still ends up convolving the images by the original pixel shape, thus blurring the final output image. Because our images are a combination of integer and non-integer dithers, astrodrizzles combination of shift-and-add and interlacing is key to reducing correlated noise while increasing final resolution.
So, using astrodrizzle we first we stacked all of the V-band images (48 images total, 4 per visit for 12 visits) to create one “deep” frame. Then, using the V-band deep frame as the reference image to which all other frames were aligned, each of the individual visits for the V- and I-bands were combined yielding 12 V-band images and 5 I-band images. Next, all of the I-band images (20 images total, 4 per visit for 5 visits) were drizzled together to create an I-band deep frame, but once again using the V-band deep frame as a reference. Finally, all of the H-band data was combined, also using the V-band deep frame as a reference. Once the exposures for each orbit are combined and all visits are all drizzled to a common reference, then it was finally time to perform photometry.
2.4 Measurements