Instrument Science Report WFPC2 97-04
Properties of WFPC2 Bias Frames
C. P. O’Dea, S. Gonzaga, M. McMaster, I. Heyer, J.C. Hsu, S. Baggett, K. Rudloff
June 3, 1997
ABSTRACT
We report on the basic on-orbit characteristics of the biases and the overscan region and
discuss the superbiases. We investigate the nature of changes (jumps) in the bias level. We
present the procedure for creation of the current set of pipeline superbiases.
1. Introduction.
The properties of WFPC2 bias frames have been discussed by the IDT in the WFPC2
Science Calibration Report using pre-launch data and have been briefly discussed using
on-orbit data by Holtzman et. al. (1995). Here we present the current state of our knowledge of the on-orbit characteristics of the bias frame and bias level.
The bias is a positive offset of about 300 DN which is added to the signal chain prior to
the Analog-to-Digital converters to prevent the signal from dropping below zero. Bias
frames are obtained by reading out the chips after clearing them. The bias overscan is
obtained by continuing to read out an additional 12 columns after all columns in a given
row of the chip are read out. The last 6 columns of the bias overscan are used to determine
a bias offset for the even and odd columns, separately, which are called BIASEVEN and
BIASODD, respectively. In the pipeline calibration, a bias image and the bias offset (even
and odd) values are subtracted from the data.
In this ISR we report on the basic on-orbit characteristics of the biases and of the overscan regions. We present an investigation of the properties of changes to the bias level
(bias jumps). We discuss the dark counts in the bias frames. We present the procedure used
to create the current set of ‘superbiases’ used in the calibration pipeline.
2. Properties of the Superbiases
Greyscale images of the superbias frames for Gain 7 are shown in Figures 1 to 4 and
plots of columns and rows are shown in Figures 5 to 12.
1
The superbiases are generally flat and well behaved in the middle (inner 200x200 pixels). There are some systematic features in the superbiases which include (1) ‘bad’
columns (both high and low), (2) occasional high pixels accompanied by fainter tails in
the direction of increasing row number, (3) a horizontal streaking seen mainly in the lower
third of the images, and (4) a drop in the bias level near the ends of the chips (e.g., last
~100 rows or columns).WF2 also has a slope across the chip (as a function of column
number) of order 0.1 DN. The superbiases have similar characteristics in the two gains.
The global structure in the two gains is also similar (e.g., the large slope across WF2),
though not identical.
When should you recalibrate using the superbias? This decision should be driven by
the need to recalibrate using the superdark, i.e., if you are using the superdark you should
use the superbias. The superdark should be used in cases where you are combining multiple long exposures of the same field in order to go deep and you are not limited by sky
background.
Figure 1: Greyscale plot of PC1 superbias, Gain=7.
Figure 2: Greyscale plot of WF2 superbias, Gain=7
4
Figure 3: Grey Scale Plot of WF3 superbias, Gain=7.
Figure 4: Greyscale of WF4 Superbias, Gain=7.
Figure 5: Column and Row Plots of PC1 Superbias Gain= 7.
.4
superbias7.c0h[1]: columns 1 to 800
superbias7.c0h[1]: rows 1 to 800
.4
.375
.38
.35
DN
DN
.36
.325
.34
.3
.32
.275
200
400
600
.25
800
200
Rows
400
columns
.38
superbias7.c0h[1][301:500,1:800]: rows 301 to 500
.37
DN
.36
.35
.34
50
100
150
columns
Inner 200 x 200 pixels of superbias image.
200
600
800
Figure 6: Row and Column plots for Superbias WF2, Gain=7.
superbias7.c0h[2]: columns 1 to 800
.34
superbias7.c0h[2]: rows 1 to 800
.4
.35
DN
DN
.32
.3
.3
.25
.28
200
400
600
800
200
Rows
400
columns
.36
superbias7.c0h[2][301:500,1:800]: rows 301 to 500
DN
.34
.32
.3
.28
50
100
150
columns
Inner 200 x 200 pixels of superbias image.
8
200
600
800
Figure 7: Row and Column Plots for Superbias WF3, Gain=7
.34
superbias7.c0h[3]: columns 1 to 800
superbias7.c0h[3]: rows 1 to 800
.325
.32
.3
DN
DN
.3
.275
.28
.25
.26
.225
200
400
600
800
200
Rows
400
columns
superbias7.c0h[3][301:500,1:800]: rows 301 to 500
.34
DN
.32
.3
.28
.26
50
100
150
columns
Inner 200 x 200 pixels of superbias image.
9
200
600
800
Figure 8: Row and Column Plots of Superbias, WF4, Gain = 7.
.36
superbias7.c0h[4]: columns 1 to 800
superbias7.c0h[4]: rows 1 to 800
.4
.375
.34
DN
.32
.325
.3
.3
.275
.25
.28
200
400
600
200
400
600
columns
800
Rows
.4
superbias7.c0h[4][301:500,1:800]: rows 301 to 500
.38
.36
DN
DN
.35
.34
.32
50
100
150
200
columns
Inner 200 x 200 pixels of superbias image.
10
800
Figure 9: Row and Column Plots of Superbias PC1, Gain=15.
.2
superbias15.c0h[1]: columns 1 to 800
.2
superbias15.c0h[1]: rows 1 to 800
.19
.18
DN
DN
.18
.16
.17
.16
.14
200
400
600
800
200
Rows
400
600
columns
.2
superbias15.c0h[1][301:500,1:800]: rows 301 to 500
DN
.19
.18
.17
.16
50
100
150
columns
Inner 200 x 200 pixels of superbias image.
11
200
800
Figure 10: Row and Column Plots of Superbias,WF2, Gain=15.
.17
superbias15.c0h[3]: columns 1 to 800
.18
superbias15.c0h[3]: rows 1 to 800
.16
DN
.15
.14
.14
.12
.13
200
400
600
800
200
Rows
400
600
columns
.17
superbias15.c0h[3][301:500,1:800]: rows 301 to 500
.16
DN
DN
.16
.15
.14
.13
50
100
150
200
columns
Inner 200 x 200 pixels of superbias image.
12
800
Figure 11: Column and Row Plots of Superbiases, WF3, Gain=15.
.17
superbias15.c0h[3]: columns 1 to 800
.18
superbias15.c0h[3]: rows 1 to 800
.16
DN
.15
.14
.14
.12
.13
200
400
600
800
200
Rows
400
600
columns
.17
superbias15.c0h[3][301:500,1:800]: rows 301 to 500
.16
DN
DN
.16
.15
.14
.13
50
100
150
columns
Inner 200 x 200 pixels of superbias image.
13
200
800
Figure 12: Column and Row Plots of Superbias, WF4, Gain=15.
.2
superbias15.c0h[4]: columns 1 to 800
superbias15.c0h[4]: rows 1 to 800
.2
.18
DN
DN
.18
.16
.16
.14
200
400
600
.12
800
200
Rows
400
columns
.2
superbias15.c0h[4][301:500,1:800]: rows 301 to 500
.19
.18
DN
.14
.17
.16
50
100
150
200
columns
Inner 200 x 200 pixels of superbias image.
14
600
800
Table 1. The Statistics of the Superbiases.
Chip
Gain
Mean
RMS
Max
Min
1
7
0.346
0.382
287.8
-0.057
2
7
0.312
0.217
91.5
-0.197
3
7
0.301
0.654
494.2
-0.147
4
7
0.330
0.183
59.5
-0.165
1
15
0.176
0.105
53.4
-0.103
2
15
0.153
0.134
69.2
-0.232
3
15
0.146
0.295
223.6
-0.117
4
15
0.170
0.100
19.7
-0.295
The mean value of the superbiases is not zero. This is due to an offset between the bias
level in the image and in the overscan. The WFPC2 IDT Calibration Report describes this
difference as ‘spurious charge.’
An analysis of the dispersion of individual pixels is in progress to determine the significance of the features in the biases.
3. Properties of the Overscan
The Overscan file (.x0h and .x0d) contains 14 columns and 800 rows. The first two
columns contain engineering data, and the remaining 12 columns are the overscan of the
chips. There are several nonuniformities in the overscan as a function of column number.
(Figure 13). (1) The first column of the overscan is generally higher by several DN than
the remaining columns. (2) There is a slope across the columns. These two effects are
thought to be due to hysteresis in the amplifier, i.e., the amplifier cannot adjust quickly
enough to the change from the generally high value in column 800 to the relatively low
value in the overscan. (3) There is occasionally an odd-even effect, where the odd and
even columns are offset by an amount which is generally small, typically less than 0.03
DN. (However, there are rare occasions where the odd-even difference is as large as 0.5
DN -- see Figures 16 and 17). Thus, the columns 9-14 are used for determining the bias
values, with average values for the odd and even columns determined separately. (This is
just a straight average of the pixels in the appropriate columns).
The mean properties of the overscans were determined using the bias images which
comprise the superbias. Mean values of the biaseven and biasodd values are given in Table
2, and plots of the values of biaseven and biasodd as a function of MJD are given in Figures 14 and 15. Histograms of biaseven - biasodd are given in Figure 16.
15
Table 2. Properties of the BiasEven and BiasOdd Values
Gain
7
15
Chip
Mean BiasEven
Mean BiasOdd
Even-Odd
Slope (DN/yr)
1
314.76 +/- 0.58
314.73 +/- 0.58
0.027 +/- 0.026
1.39
2
353.31 +/- 0.52
353.29 +/- 0.52
0.015 +/- 0.025
-0.04
3
305.26 +/- 0.57
305.30 +/- 0.57
-0.038 +/- 0.023
-0.40
4
310.64 +/- 0.58
310.63 +/- 0.58
0.008 +/- 0.023
-0.42
1
297.79 +/- 0.42
297.77 +/- 0.42
0.017 +/- 0.018
0.25
2
318.19 +/- 0.37
318.18 +/- 0.37
0.006 +/- 0.020
-0.64
3
305.95 +/- 0.32
305.96 +/- 0.32
-0.015 +/- 0.017
1.11
4
304.50 +/- 0.27
304.46 +/- 0.27
0.037 +/- 0.030
-0.66
We find that there is a scatter in the values of biaseven and biasodd of roughly 0.3 DN.
However, the difference between biaseven and biasodd is near zero with a dispersion of
roughly 0.025 DN. The difference is slightly positive in PC1, WF2, and WF4, and slightly
negative in WF3. The results for gain 7 and 15 are similar. We note that there is a slight
drift in the values of biasodd and biaseven with time. The slope varies from chip to chip
and is different in gain 7 and 15 and has a maximum value of about 1 DN per year.
In order to examine trends in the values of biaseven and biasodd over a longer period
of time we extracted these values from 1272 darks in gain 7 going all the way back to the
cooldown in March 1994. The values of biaseven, biasodd, and their difference are plotted
in Figures 17, 18, and 19. The overall mean values and trends of the biaseven and biasodd
values in these darks are similar to those in the biasframes for the times in which they
overlap. We note two important differences seen in the longer dataset on the darks. (1) The
slopes of the drifts in biaseven and biasodd are not constant with time. (2) There are occasional excursions of the values of the biaseven - biasodd of the order of 0.5 DN.
In general, the overscans are well behaved and to a good approximation constant as a
function of row number (Figure 20). Thus, subtraction of a single value for even and odd
columns is generally sufficient. However, small amplitude changes in the overscan level,
“bias jumps” are occasionally seen. Bias jumps are discussed in more detail in the next
section.
16
Figure 13: Plots across overscan region showing structure as a function of column.
u3cu0101t.x0h[1][3:14,*]: rows 100 to 700
u3cu0101t.x0h[1][4:14,*]: rows 100 to 700
315.4
319
315.3
318
315.2
317
315.1
316
2.5
2.5
5
7.5
5
10
7.5
10
Column (pixels)
Column (pixels)
Shows first column of overscan is too high.
Shows slope across overscan.
u3cu0104t.x0h[4][6:14,*]: rows 100 to 700
310.74
310.72
310.7
310.68
2
4
6
8
Column (pixels)
Shows odd-even ‘sawtooth’ pattern seen in some overscans.
17
Figure 14: Time Dependence of Biaseven (left panel) and Biasodd (right) for Gain=7.
18
Figure 15: Time Dependence of Biaseven (left panel) and Biasodd (right) for Gain=15.
19
Figure 16: Histogram of BiasEven - BiasOdd for the Bias frames in the Superbias: Gain 7
(Left) and Gain 15 (Right).
BiasEven - BiasOdd for 160 Biases (PC1, gain = 7)
BiasEven - BiasOdd for 160 Biases (PC1, gain = 15)
80
100
60
80
60
40
40
20
20
0
0
-0.2
-0.1
0
0.1
0.2
-0.2
-0.1
DN
0
0.1
0.2
0.1
0.2
0.1
0.2
0.1
0.2
DN
BiasEven - BiasOdd for 120 Biases (WF2, gain = 7)
BiasEven - BiasOdd for 120 Biases (WF2, gain = 15)
80
60
60
40
40
20
20
0
0
-0.2
-0.1
0
0.1
0.2
-0.2
-0.1
DN
0
DN
BiasEven - BiasOdd for 120 Biases (WF3, gain = 7)
BiasEven - BiasOdd for 120 Biases (WF3, gain = 15)
80
80
60
60
40
40
20
20
0
0
-0.2
-0.1
0
0.1
0.2
-0.2
-0.1
DN
0
DN
BiasEven - BiasOdd for 120 Biases (WF4, gain = 7)
BiasEven - BiasOdd for 120 Biases (WF4, gain = 15)
80
60
60
40
40
20
20
0
0
-0.2
-0.1
0
0.1
0.2
-0.2
-0.1
DN
0
DN
20
Figure 17: Plots of Time Dependence of BiasEven and BiasOdd from 1272 Darks (Gain=
7).
21
Figure 18: Plot of BiasEven - BiasOdd for 1272 Darks at Gain=7.
22
Figure 19: Histograms of BiasEven - BiasOdd for 1272 Darks (Gain = 7).
BiasEven - BiasOdd for 1272 Darks (PC1, gain = 7)
BiasEven - BiasOdd for 1272 Darks (WF3, gain = 7)
1000
800
600
600
400
400
200
200
0
0
-0.4
-0.2
0
0.2
0.4
-0.4
-0.2
DN
0
0.2
0.4
0.2
0.4
DN
BiasEven - BiasOdd for 1272 Darks (WF2, gain = 7)
BiasEven - BiasOdd for 1272 Darks (WF4, gain = 7)
1000
800
800
600
600
400
400
200
200
0
0
-0.4
-0.2
0
0.2
0.4
-0.4
DN
-0.2
0
DN
23
Figure 20: Examples of typical overscans, Gain = 7.
316
u3cu0101t.x0h[2]: columns 9 to 14
u3cu0101t.x0h[1]: columns 9 to 14
353
315.5
DN
DN
352.5
315
352
314.5
200
400
600
351.5
800
200
400
600
800
Rows (pixels)
Rows (pixels)
u3cu0101t.x0h[3]: columns 9 to 14
u3cu0101t.x0h[4]: columns 9 to 14
305.5
310.5
DN
DN
305
310
304.5
309.5
304
200
400
600
800
200
Rows (pixels)
400
Rows (pixels)
24
600
800
4. Bias ‘Jumps’
Column plots of the overscan showing examples of changes in the overscan level are
given in Figure 21. Generally four broad types of bias changes are seen. (1) Sharp steps in
bias level. (2) Troughs or steps which return to the previous level. (3) Ramps, in which the
change in bias level occurs gradually over tens of rows. (4) Ripple, in which the bias level
wanders around on scales of tens or hundreds of rows. Examination of bias jumps by eye
suggests that the first two types of jump predominate.
During Thermal Vacuum testing, bias jumps were seen in WF4 because a major frame
boundary (with consequent WFPC2 activity which affected the electronics) ocurred during WF4 readout. On orbit, all the readouts occur within a single major frame. Thus bias
jumps on orbit were unexpected. Some bias jumps may be due to interference with the
electronics by other activities on the spacecraft. In addition, some bias jumps are due to
the presence of high intensities in the corresponding image (e.g., saturated stars) and the
efffects of hysteresis in the amplifier (see WFPC2 Science Calibration Report, 1993; and
the discussion in the previous section).
As part of the effort to automate the data quality assessment of HST data, the WFPC2
group has developed an algorithm to identify bias jumps in WFPC2 images. In the past,
these regions were identified by members of the OPUS staff who manually looked at each
image on a workstation and informed the observers of the anomaly. As of August 9, 1996,
the calibration pipeline performs this task automatically. Besides saving manual labor, the
new software provides a more objective and more sensitive method of finding bias jumps.
Currently our procedure is the following. Note that the exact criteria may evolve as we
gain more experience with bias jump detection. In CALWP2, a routine examines a subset
(from column 5 to 14) of the overscan file (.x0h file) and determines and compares the
mean values in 8 bins along the columns, i.e., row 1-100, 101-200, ..., 701-800. Any
change in the bias level greater than 0.09 DN between any two bins is noted in the trailer
and header files for the images. For jumps greater than 0.5 DN a warning message is
issued in these two files and is also placed in the PDQ file for that observation. Note that
large changes in the bias level can occur if there is a highly saturated star or missing data
in the image. Currently, the message is purely informational and no correction is done
within CALWP2. The bias jumps can be corrected by the observer if so desired using separate bias levels for the appropriate ranges of rows.
We have used the automatic detection routine to determine the properties of bias jumps
in bias images. The statistical analysis was performed on the .x0h files of bias images, in
order to avoid spurious bias jumps caused by high signal in the images. The statistics refer
to the number of bias jumps above a detection threshold of 0.1 DN. The algorithm can
only report one jump per chip, so the cases of multiple jumps per chip are not reported.
25
Table 3 gives the statistics for the fraction of datasets with at least one jump in at least one
chip.
Table 3. Frequency of Occurence of Bias Jumps per Dataset in Bias Images.
Year
Gain
Total Datasets
Fraction
1994
7
195
66%
1995
7
246
67%
1996
7
168
57%
1994
15
194
56%
1995
15
194
47%
1996
15
168
33%
We see that bias jumps occur a significant fraction of the time in both gain settings.
Gain 7 and 15 have frequencies of ~63% vs ~45%, respectively. Jumps occur less frequently in Gain 15. In Table 4, we present the statistics for frequency of occurrence of at
least one jump with amplitude above 0.1 DN per chip.
Table 4. Frequency of Occurrence of Bias Jumps per Chip in Bias Images.
Year
Gain
PC1
WF2
WF3
WF4
1994
7
41%
10%
22%
19%
1995
7
41%
11%
17%
19%
1996
7
37%
12%
13%
9%
1994
15
11%
12%
4%
38%
1995
15
6%
11%
7%
36%
1996
15
4%
4%
1%
27%
Table 4 shows that bias jumps occur a significant fraction of the time in all chips,
though the number of jumps is not uniformly distributed among the chips. PC1 dominates
the statistics in Gain 7, and WF4 in Gain 15. The overall frequency of occurrence may be
dropping slightly with time. This will need to be followed to confirm this suggested trend.
We have also determined the mean value of the bias jumps detected by the algorithm
(Table 5). The distribution of the jump amplitudes is shown in Figure 22 for the bias
frames from 1994. The distribution shows a peak at low amplitudes, a flat tail to higher
values, and then a relatively sharp cutoff at values between 0.16 and 0.20 (though WF4 in
Gain 15 extends to higher amplitudes than the others). The average for Gain 7 and 15 are
0.127 and 0.147, respectively. Thus, we find that in the absence of a strong signal in the
26
chip, the bias jumps tend to have low amplitude, with jumps above 0.1 DN having an
average amplitude of about 0.14 DN. There is no signficant difference in the jump amplitude for Gain 7 and 15, i.e., the factor of two expected if the jumps are proportional to the
number of electrons in the signal is not seen. And there is no significant time dependence
in the jump amplitude.
Table 5. Amplitude of Bias Jumps per Chip.
Gain
Year
Chip
Mean
STD
NPTS
7
1994
PC1
0.139
0.109
80
1995
0.151
0.126
101
1996
0.138
0.088
62
0.106
0.005
20
1995
0.134
0.072
27
1996
0.130
0.083
20
0.134
0.078
42
1995
0.117
0.020
43
1996
0.118
0.017
21
0.116
0.014
38
1995
0.130
0.060
47
1996
0.118
0.021
16
0.122
0.040
22
1995
0.114
0.017
11
1996
0.184
0.132
6
0.110
0.009
23
1995
0.139
0.078
21
1996
0.117
0.011
7
0.153
0.118
7
1995
0.167
0.096
13
1996
0.109
0.005
2
0.185
0.345
73
1995
0.167
0.096
13
1996
0.203
0.434
46
1994
1994
1994
15
1994
1994
1994
1994
WF2
WF3
WF4
PC1
WF2
WF3
WF4
27
Figure 21: Column plots showing examples of bias level changes in the images (left) and
the corresponding overscan (right). Note the different types of bias changes: steps,
troughs, ramps, and ripples.
u2sl1706t.d0h[4]: columns 100 to 700
u2sl1706t.x0h[4]: columns 6 to 14
305.5
305
304.8
304.5
304.6
304
304.4
303.5
200
400
600
800
303
200
Line (pixels)
305.5
400
600
800
Line (pixels)
u2sl1n05t.d0h[4]: columns 100 to 700
u2sl1n05t.x0h[4]: columns 6 to 14
305.5
305.25
305
305
304.5
304.75
304
304.5
304.25
200
400
600
800
303.5
200
Line (pixels)
309
400
600
800
Line (pixels)
u2gr0301t.d0h[3]: columns 100 to 700
u2gr0301t.x0h[3]: columns 6 to 14
308
308.5
307
308
307.5
306
307
306.5
200
400
600
800
305
Line (pixels)
200
400
Line (pixels)
28
600
800
Figure 22: Histogram of Bias Jump Amplitudes in Bias Images in 1994. Gain 7 (left) and
Gain 15 (right). Note the long tail of the distribution to higher values. The last bin contains
all values greater than or equal to 0.185.
29
5. The Dark Current in the Biases
Recall that activities on WFPC2 are synchronized to start at 60s intervals called
“major frames.” For the bias images, after the initial erase (which takes 16.4s), the chip
then integrates for zero seconds and then the chips are read out at the start of the next
major frame. The pixels accumulate dark counts from the time they are erased until the
time they are read out. It takes 22 microsecs to clock out each pixel from the parallel to the
serial registers or 18.12 millisecs per row, so for 800 rows it takes 800*0.01812 = 14.5s to
read out one chip. The chips are read out simultaneously during the initial erase and
sequentially 1-4 during the final read out with an additional 50 millisecs delay between
chips. Thus, the total dark time for a given row is
(n - 1)*(14.5 + 0.05) + (m-1)*0.01812 + 43.6s
where n is the chip number (1-4) and ‘m’ is the row number (1-800). Since the dark
current is not subtracted from the bias frames, the bias frame subtraction in the pipeline
serves to remove the dark current from the science image for the corresponding timeframes, i.e., the dark count in the first frame and the readout delay. As discussed in the
WFPC2 Handbook, Section 4.8.2, this is a reasonable thing to do as long as the dark current is constant over the timeframe between the bias frames and the science data. Given
that the bias frame subtraction removes some of the dark counts, the appropriate remaining dark time to subtract from an image is
darktime = 60s (if clocks=no and exptime>180s) + 60*INT((exptime+16.4)/60) s.
Currently, the DARKTIME used for the dark subtraction is taken to be equal to the
exposure time. In general, for exposures (> 180s) in which the clocks are off, the actual
dark time will be longer by 60s than the exposure time.
6. Appendix A: Procedure for Creating the SuperBias
Initially 40 bias frames were used to create the pipeline bias image. In the analysis of
the Hubble Deep Field data it was realized that much larger numbers of bias frames could
be combined to create a “superbias”. The first set of superbiases created using 120 bias
frames using the following procedure was released for use in the pipeline on January 13,
1997.
1. Retrieve more than 120 *raw* bias files from the archive.
2. Divide the bias files into batches of 40 (IRAF processing limitation)
3. Recalibrate raw bias files. Use “chcalpar” to set the mask correction, a-to-d correction, and bias level correction switches.
MASKCORR=PERFORM
ATODCORR=PERFORM
BLEVCORR=PERFORM.
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Then use “calwp2” to recalibrate the bias files.
4. After recalibration, find out which bias files had bias jumps. The jumps are found
by “calwp2”, and documented in the history section of the calibrated header file
(.c0h) (For example, “grep level *.c0h > bjumps”). Sort the list of files in order of
severity of jumps, and use the best 40 bias files. Since the jumps are small, typically 0.1 DN, the result of combination of the 40 files is not affected by remaining
jumps.
5. Display each image using the task “display_all.cl” to look for missing data, residual images, or other anomalies.
6. For each chip, create plots for “biaseven vs. date” and “biasodd vs. date” to check
for anomalies in the global bias level, and make sure the scatter looks reasonable.
7. Combine the 40 bias images using “mkdark” (old version of “crrej”) to create a
combined bias file, and a mask file.
8. Repeat steps 3-7 for the other two 40-file batches. This will give three combined
bias images with their mask files; each combined bias was created from combining
40 bias frames.
9. Combine again the three files created in step 8 into a superbias representing the
average of 120 bias files.
10. The mask files created as a byproduct of “mkdark” (see item 8), have the following
information: Each pixel value corresponds to the number of individual bias files
used to create the corresponding pixel in the combined bias image, ie. if the value
of pixel (50,50) in the mask file is 30, it means that 30 pixels were used to create
the combined-bias pixel (50,50), and 10 pixels were rejected.
Use the mask files created in the three batches to create a data quality file for the superbias. Add the three mask files together to represent the data quality of all 120 bias files.
11. To create a data quality file for the superbias, determine a cut-off for the number of
pixels from 120 bias files that would be used to create a superbias. In the case
below, a superbias pixel created from 100 and more bias pixels is flagged as good
(value=0). All other pixels are set to bad (value=2).
12. Compare the newly-created pipeline bias and data quality files with previous files.
If they are significantly different, try to figure out what’s causing it.
13. Edit the header to conform with standard header (ICD47) and add history records.
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7. Appendix B: Time Line of Bias-Related Events
Date
Event
Mar-08-94
Overscan used in CALWP2 changed from 3:14 to 9:14
May-04-94
Separate corrections for odd and even rows installed in CALWP2
Aug-09-96
Automatic checking for Bias Jumps installed in CALWP2
Jan-13-97
First Superbiases of 120 frames released to the pipeline
Acknowledgements
We are grateful to Harry Ferguson, Massimo Stiavelli, and Sito Balleza for helpful discussions and to Stefano Casertano for comments on the report.
References
Biretta et al., 1996, WFPC2 Instrument Handbook, Version 4.0
Holtzman et al. 1995, PASP, 107, 156
WFPC2 IDT, 1993, WFPC2 Science Calibration Report, Prelaunch Version 1.2
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