Column To Column Variation in ACS/WFC
The ACS team has determined the column dependent variation in the CTE trails of ACS WFC and found that 81% of columns fall within 10% of the average, and 96% fall within 20%. Applying these values to the current CTE algorithm will scale the level of CTE correction for each column with the number of traps in each column. This has been implemented in CALACS by creating a look up table that will adjust the CTE correction by the final column dependence values.
We have measured the value of the CTE trails in each column using the virtual overscan region of multiple flat field images. The first few pixels of the virtual overscan contain a trail produced by the flux pixels on the edge of the image. As these flux pixels are read out they pass every trap present in the column, making their trail an accurate reflection of the number of traps in that column. These column specific measurements will be incorporated into the new version of CALACS.
We started with somewhere between 3 to 30 raw flat field images for each year, from 2002 to 2011. From each of these flat field images we pulled out a measurement of the CTE trail for each column. We identified the first pixel of the virtual overscan region (see Figure 1) as an ideal place to measure a CTE trail as the only other contributing signal to these pixels is the applied bias. We subtracted the bias by taking an average of the first 11 pixels of the virtual overscan region in each column and subtracting this from the remaining column. This means we attained a column specific bias. For our final measurement we compared the CTE trail in one column, to the CTE trail in the surrounding columns using a 50 pixel wide boxcar average.
Figure 1: Schematic of CCD 2 of WFC on ACS, each trail pixel has been bias subtracted using the average of the bias pixels shown, and is compared to the 25 columns to either side of the column being measured
Figure 2: Cut out of a raw flat image, taken from the top overscan region of columns 2874-3020. White represents high intensity, black represents low intensity. At this stretch it is possible to see the various amounts of trailing in the virtual overscan.
Column Variation With Time
The first step of our data reduction after subtracting the bias was to combine the multiple data points within each year into an average for that year. Figure 3 shows the average distribution of points for the year 2009.
Figure 3: This graph shows column number versus the percent difference between one column and the boxcar average. Data for each column is taken from the six 2009 flat images, so that each column is composed of six data points.
For each column we took an average of all points in one year, using the standard deviation as the error value. This average was then compared to the boxcar average, and a percent difference was taken. Figure 4 shows the results of this for a standard column in the WFC2 CCD, column 2603 for the years 2002 - 2011.
Figure 4: Year (2002 - 2011) versus percent difference between column 2603 and the boxcar average.
The next step in our reduction was to condense the data from all years into one final data point per column. By visual inspection we determined that the trail values for years 2002 - 2007 had a large error and large variation over time. This is expected as the column variations due to CTE loses should not be significant over this time period. To obtain our final values we took an average of the 2009 - 2011 data.
Figure 5 presents our final results. 96% of the columns lie between 1.2 and 0.8 for both CCDs, meaning a typical column does not vary from the average by more than 20%. Outliers have been confirmed as hot columns.
Figure 5: The top graph shows the final column to column variation for CCD 2 on WFC. These points are the average of the values calculated from 2009 - 2011. The error bars represent the standard deviation of the measurements taken from each flat field added in quadrature. The bottom graph shows an enlargement of column 2650 to column 2700.