------------------------------------------------------------------- The Hubble Ultra Deep Field ACS/WFC Combined Images - V1.0 ========================================================== Space Telescope Science Institute Date: Tuesday, 9th March 2004 Initial Document written by: Anton M. Koekemoer Incorporating Revisions by: Steven V. W. Beckwith 1. Introduction The Hubble Ultra Deep Field (PI: Steven V. W. Beckwith) is a 400-orbit Cycle 12 program to image a single field of the Wide Field Camera (WFC) of the Advanced Camera for Surveys (ACS) in four filters: F435W (B), F606W (V), F775W (i), and F850LP (z). The observations took place over 4 months from September 2003 to January 2004 under two program IDs: 9978 and 10086. The observations consist of half-orbit exposures, cycling through each of the filters in a 4-point dither pattern to provide sub-pixel sampling, as well as a larger-scale 3-point line pattern to cover the 2 second of arc gap between the two ACS/WFC chips. The total exposure times are summarized below, with typical exposure times of 1200s for individual images. The AB magnitude zeropoints for ACS are current as of March 2004. Number of Number of Total Exp. AB mag. Orbits Exposures Time (s) zeropoint B (F435W): 56 112 134880 25.673 V (F606W): 56 112 135320 26.486 i (F775W): 144 288 347110 25.654 z (F850LP): 144 288 346620 24.862 The observations used two primary roll angles separated by 90 degrees in each of two epochs. Each of the two epochs contained observations at two roll angles differing by 4 degrees. Thus, there were four roll angles in all. A roll angle, PA, refers to the orientation on the sky of the -V3 axis of the spacecraft (thus the +ve y-axis of the ACS chips). For each filter, the number of orbits for a particular date range / PA combination are: Observation Dates PA B V i z Tot. orbits 09/24/2003 - 10/02/2003 310 6 8 18 18 50 10/03/2003 - 10/28/2003 314 22 20 58 56 156 12/04/2003 - 12/22/2003 40 6 8 18 20 52 12/11/2003 - 01/15/2004 44 22 20 50 50 142 Total orbits: 56 56 144 144 400 The data were initially calibrated using the "calacs" software (ACS Data Handbook, Mack et al. 2002), and subsequently combined using a pipeline version of the MultiDrizzle software (Koekemoer et al. 2002). Here we provide more details on all these steps. 2. Data Processing and Calibration Each of the 800 ACS/WFC exposures was processed through the standard HST data pipeline, OPUS. Each exposure was first calibrated using the "calacs" software within STSDAS. Calacs removes the bias level, subtracts the dark current, corrects the flat field and gain, flags known bad pixels, and does an initial photometric calibration. Special calibration files were created for these observations: * Hyperdarks were created using all the dark-current frames from the 6-month period encompassing the UDF observations. These files (provided by M. Mutchler of the ACS team) provide a higher S/N than the usual dark reference files that are subtracted during standard calibration and give better overall correction at the depths of the UDF. * New flatfield images were made using the "L-flat" technique based on stellar photometry of 47 Tucanae (provided by J. Mack of the ACS group). These flatfield images produced a more uniform sky level across the images than the standard pipeline products down to ~1 - 2% of the sky brightness. * Sky-flats were generated to correct the ~1-2% residuals that remain after calibrating the data with the L-flats. For each band, the sky flats resulted from six data processing steps: 1) An image of the field was created from the pipeline data by combining all exposures. 2) This image was used to create a mask identifying the spatial extent of all the astronomical objects in the image; this process also provided cosmic ray masks. 3) A median image was created from the original calibrated exposures using the combined object masks and cosmic ray + bad pixel masks to exclude all pixels affected by celestial sources, cosmic rays or bad pixels. 4) The median image was averaged over 100 pixels (convolved with a 100 pixel smoothing function), corresponding to the spatial frequency of the flat-field residuals. 5) The calibrated files were divided by this smoothed median image to produce an image of the sky. 6) This sky image was subtracted from each individual calibrated image. * A bad-pixel file, containing a number of additional bad columns and other defects not present in the standard data quality arrays when the data were processed. The bad pixel file for each band was created after correcting the images with the improved sky values in the previous step, then subtracting an image made by combining all exposures. The resulting images made it easy to identify bad pixels and cosmic rays that were significantly above the noise level. A difference image was then created for each exposure, expressing the difference in terms of the underlying r.m.s. of each pixel (including Poisson noise from any objects). All 800 images were subsequently combined using both median and averaging, to produce an image that showed the median or average deviation of each pixel. Sigma-clipping was used to identify additional bad pixels. This technique was especially valuable in identifying charge traps not seen in the original data quality file. In addition, the bias level correction performed by "calacs" (see ACS Data Handbook 2002, Mack et al.) did not fully remove the bias levels in the 4 amplifier quadrants, but left slight residual offsets of a few tenths of an ADU. These were corrected by means of a special script (provided by M. Sirianni, ACS group) which first reversed the multiplicative flatfield correction, then solved iteratively for the residual bias differences between the quadrants and removes these, before re-applying the flatfield. The last remaining effect in the data is an apparent amplifier cross-talk between the four amplifiers of the ACS/WFC chips, which produces very low level dark "ghosts" evident as mirror images of bright objects in the other quadrants. Some efforts have been made to understand this effect (M. Giavalisco et al., in preparation) and the ACS group is currently in the process of constructing a well-understood physical model for it. However, its amplitude is very low, and detailed examination of objects in the affected regions showed that its effect on the data is well below any scientifically significant level. 3. Astrometric Correction Astrometric corrections were applied to the individual exposures after removing the instrumental signatures from individual exposures as described above. We used a set of distortion correction images (Anderson et al. 2003) to remove the residual distortion not accounted for by the 4th-order polynomial that is used for astrometric calibration in the standard ACS pipeline (Cox, 2002, ACS-ISR-02-02). R. Hook created a version of drizzle, called wdrizzle, that used the information from these distortion residual images when drizzling each input exposure onto an astrometric grid. The output images were aligned to accuracies better than 0.05 - 0.1 ACS pixels (2.5 - 5 milliseconds of arc) across the entire field of view. Once image distortion had been removed by this procedure, the separate exposures were aligned on a common astrometric grid by using an external reference frame derived from the GOODS dataset (Giavalisco et al. 2003.) This method is more accurate than the standard use of guide stars to align the separate exposures. The UDF is projected onto the same tangent point as the GOODS reference frame defined by the pointing center of the Spitzer IRAC and MIPS ultra-deep imaging that is being obtained in this part of the sky. A set of GOODS images was produced that overlapped the UDF (Koekemoer et al) and catalogs were generated based on these images. The catalogs from the GOODS ACS/WFC images were used as the astrometric baseline for registering each UDF exposure. Each exposure was cleaned of cosmic rays using a single-image cosmic ray rejection algorithm, then is was compared with a catalog to match objects within 2 seconds of arc of GOODS sources. Typically 200 - 800 objects matched, depending upon the band. The resulting differences between the object positions - GOODS vs. UDF - were used to determine a shift and rotation correction for each exposure, typically yielding overall accuracies of about 0.0002 degrees for the rotation and 0.1 pixels for the shift. This accuracy was sufficient to continue with the cosmic ray rejection and produce a first-pass UDF image. After a first-pass combined UDF image had been produced with cosmic ray masking, each of the input exposures was modified by replacing each of pixels affected by cosmic rays with the values from the combined UDF image. This procedure yielded a clean set of input exposures to compare with the GOODS catalog for improved astrometric registration. After this step, the residual error in the rotation was less than 0.0001 degrees, and the residual translation errors were less than 0.01 pixels r.m.s. (500 microseconds of arc.) These astrometric shifts and rotations appear in the WCS header keywords for each input exposure. Finally, the cosmic ray and final drizzle combination were repeated. 4. Cosmic Ray Rejection Each exposure was transformed onto a separate set of rectified, registered output images, using the "drizzle" task to remove the geometric distortion, and using the new updated astrometric keywords to ensure accurate registration. The rectified image for each band were then combined to create a clean median image using sigma-clipping to remove pixels with values many standard deviations from the mean. The clean median image was then transformed back to the distorted frame of each input image using the WCS-based "wblot" program provided by R. Hook, effectively applying the inverse transformation to "wdrizzle". The input image was then compared with this clean image, as well as with its derivative, to reject pixels deviating from the clean image using the following algorithm: abs(Input - Clean) > scale*deriv + SNR*sqrt( rn*rn + gain*abs(Clean + background) ) / gain where "scale" was set to 1.5, "deriv" is the derivative image, "SNR" was set to 3.5 (corresponding to 3.5 sigma), "rn" was the read-out noise, and background was the sky value measured in the original input image. This procedure is essentially sigma-clipping, with the addition of the derivative image which helps to soften the rejection and prevent pixels being incorrectly identified as cosmic rays in regions of extremely sharp gradients, such as near bright stars. A second iteration was carried out using somewhat more stringent cuts, masking additional pixels around those masked in the first pass. This algorithm is the standard "driz_cr" that was initially implemented for the Hubble Deep Fields (Fruchter and Mutchler); the parameters were tuned for the UDF data by a process of iteratively testing the effects of different combinations to ensure robust rejection of all cosmic rays whlie at the same time avoiding over-rejection of good pixels in bright objects. 5. Image Combination The cosmic ray masks were then used as input to the final drizzle combination of all the images. The "drizzle" program (Fruchter and Hook 2003) essentially performs a weighted sum of the input images, and allows input pixels to be shrunk by a specific amount before being mapped onto the output plane. The final pixel scale was set to 0.6 of the input ACS pixels or 30 mas/pixel. Since this scale provides Nyquist-limited sampling of the PSF in all 4 bands, it is the optimum pixel size to use. The images were weighted before combination. The weights were the inverse variance of each exposure. The inverse variance has the appealing property that the final summed image is properly weighted with a resulting output inverse variance image that is simply the sum of all the input inverse variance images. An input variance image was calculated separately for each exposure, taking into account the flatfield variation, the sky level, and all the bad pixel information. Since the UDF has such a large number of pointings in each band with a good sampling of the sub-pixel space, the final images were drizzled using a point kernel (i.e. setting "pixfrac" = 0), corresponding to pure interlacing, so that each input pixel maps onto only a single output pixel. This choice has the advantage of minimizing the amount by which the output image has been processed (convolved with a smoothing function), thus providing the sharpest possible image. The images are oriented with North toward the top (increasing y-axis values.) The tangent point for the UDF, defined as the point at which the reference pixel of the image intersects the spherical sky, is located at the tangent point for GOODS: RA = 53.122751, Dec = -27.805089 (J2000). This tangent point defines the astrometric grid for the UDF and also for the GOODS multi-waveband dataset in this portion of the sky. It is recorded in the CRPIX and CRVAL keywords for the images, as follows: CRVAL1 = 53.122751 CRVAL2 = -27.805089 CRPIX1 = 9470.5 CRPIX2 = 3610.5 Note that the reference pixel values are set to half-integer values. As a result, the corner, rather than the center, of the input pixels were mapped to the reference pixel when drizzle was run, thus better facilitating down-sampling or block-averaging of the image. In this reference frame, the image center is at the UDF target coordinates of RA = 03:32:39.0, Dec = -27:47:29.1 (J2000) while the reference pixel in the image corresponds to the GOODS tangent point. The final ACS/WFC data products consist of 2 images for each of the four bands: Filename Description h_udf_wfc_b_drz_img.fits F435W drizzled image (electrons/s) h_udf_wfc_b_wht_img.fits F435W inverse-variance weight image h_udf_wfc_v_drz_img.fits F606W drizzled image (electrons/s) h_udf_wfc_v_wht_img.fits F606W inverse-variance weight image h_udf_wfc_i_drz_img.fits F775W drizzled image (electrons/s) h_udf_wfc_i_wht_img.fits F775W inverse-variance weight image h_udf_wfc_z_drz_img.fits F850LP drizzled image (electrons/s) h_udf_wfc_z_wht_img.fits F850LP inverse-variance weight image Each image is 10500x10500 pixels (total size 430 Mb each), with a pixel scale of 0.03"/pixel, and oriented with North toward the top. 6. Cataloging We used the SExtractor program (Bertin & Arnouts 1996) to produce catalogs from the final drizzled ACS/WFC images, using the inverse variance images as input for calculating the r.m.s. uncertainties associated with the flux measurements for each source. The catalogs were initially run on the i-band image, which is our deepest image and thus provides the most complete sample of sources. Subsequent catalogs were produced in "dual-image" mode, using the i-band isophotes of each source to measure its photometry in the other bands, thereby producing isophotally matched magnitudes that can be directly compared to produce colors for each source. The SExtractor parameters were optimized for the characteristics of the UDF ACS/WFC images, specifically our pixel scale and PSF. We used a seeing FWHM of 0.09 seconds of arc, and required a minimum of 9 contiguous pixels with a detection threshold above 0.61 sigma, with a total of 32 deblending sub-thresholds and a contrast parameter of 0.03. More details on these and other parameters will be presented in Beckwith et al. (2004, in preparation). Our output parameters are: - source ID number - X and Y pixel location of the source on the UDF images - Right Ascension and Declination (J2000) - source orientation "Theta" (counterclockwise from the X-axis) - ellipticity (1 - B/A, where A is the semi-major axis) - R50, the half-light radius (in pixels) - FWHM of a Gaussian fit to each source (in pixels) - Stellarity (1 for point sources, 0 for fully resolved sources) - AB magnitudes for each band (B,V,i,z), isophotally matched - formal AB magnitude errors (from rms map + Poisson statistics) - S/N (dividing the measured flux by the r.m.s., which includes all the information from the inverse variance weight map). The formal i-band catalog contains a total of 10,040 sources. A visual inspection of all the sources revealed an additional 5 spurious sources (which do not form part of the catalog). Moreover, the deblending algorithms in SExtractor caused an additional 100 sources to be missed, owing to their proximity to brighter sources. These sources were identified manually, and formally added by doing another SExtractor run with considerably different deblending parameters, in order to detect them all. An initial list of 208 sources was produced, which was then reduced to a total of 100 sources after visual inspection and rejection of sources that were clearly part of previously identified sources. These additional sources are denoted by ID numbers 20001 - 20208. Although the i-band image is our deepest image, there remain additional sources that were not detected in i-band, even though they may be detected in one of the other bands. Therefore we produced a second catalog based on detection in the z-band image, and we include from this catalog an additional 39 sources that are detected at > 10 sigma in the z-band image, but are not in the catalog that was run using the i-band image for detection. These additional sources are denoted by ID numbers above 30000. This catalog is accessible as the file: h_udf_wfc_V1_i_cat.txt We also present a z-detected catalog which has the same format as the i-detected catalog, namely with magnitudes, errors and S/N values in the other 3 bands (in this case B, V, i) that are measured within the same isophotes as those detected in the z-band. This catalog is accessible as the file: h_udf_wfc_V1_z_cat.txt Finally, we present segmentation maps for both the i-detected and the z-detected catalogs, which are called: h_udf_wfc_i_seg_img.fits.gz h_udf_wfc_z_seg_img.fits.gz These segmentation maps are integer FITS files, on the same scale as the full-size UDF images (30 milliseconds of arc / pixel), and with pixel values set to 0 where there are no objects, otherwise set to an integer corresponding to the ID number of the object in the catalog, thereby mapping out on the image the spatial extent of each object in the catalog. We caution that these catalogs are highly preliminary; they are likely to be representative of most of the sources across the field, but we have not had the opportunity to perform detailed statistical analyses or simulations of their completeness and related properties, particularly toward fainter magnitudes. Similarly, the S/N values are likely correct to no more than about 5-10%, depending upon the detailed structure of the weight map underlying each object. Therefore we urge the community to consider performing independent cataloging analyses if these effects are critical to the science that the data are used for. References: Anderson, J. 2002, HST Calibration Workshop, Eds. S. Arribas, A. M. Koekemoer, B. Whitmore (STScI: Baltimore), p. 13 Beckwith, S. V. W. et al. 2004, in preparation Bertin, E. & Arnouts, S. 1996, A&AS 117, 393 Cox, C., 2002, Instrument Science Report ACS-ISR-02-02 Fruchter, A. S. & Hook, R. N. 2002, PASP 114, 144 Giavalisco et al. 2003, astro-ph/0309105 Koekemoer, A. M., Fruchter, A. S., Hook, R. N., & Hack, W. 2002, HST Calibration Workshop, Ed. S. Arribas, A. M. Koekemoer, B. Whitmore (STScI: Baltimore), p. 337 Mack, J. et al., 2002, ACS Data Handbook -------------------------------------------------------------------