THE SENSITIVITY AND NOISE
OF THE APO CLOUD CAMERA
J. E. Gunn, M. A. Carr, S. Snedden, J.
Brinkmann
November 29, 2001
A simple apparatus to calibrate the cloud camera was constructed in
October 2000, consisting of a 30-inch square aluminum plate mounted
on an insulating backing, with an isolated 10-inch square insert in
the center which can be heated with resistors distributed over its
back. The whole apparatus is suspended over the cloud camera, with
the small heated plate directly over the input mirror. The heated
plate subtends an angle of 36 degrees as seen from the input mirror,
measured along either of its principle chords. The temperature
difference between the heated plate and the aluminum surround was
measured by a Fluke dual themocouple meter; the relative zero was
checked before and after the measurement.
The measurements were done on the night of
Nov 9-10 MST. The log of the temperature differences between the
heated plate and the surround is as follows:
Time
|
Temp1 - Temp2
(C)
|
Comments
|
1818
|
2.7
|
camera started
|
1829
|
1.7
|
image fetch choked
|
1838
|
1.1
|
restarted
|
1844
|
0.8
|
|
1850
|
0.6
|
|
1901
|
0.1
|
|
1911
|
0.0
|
|
1932
|
0.2
|
|
1940
|
0.3
|
Power supply ON
|
1951
|
1.7
|
|
2004
|
2.9
|
|
2016
|
3.5
|
|
2024
|
3.8
|
|
2032
|
4.1
|
|
2044
|
4.2
|
|
2049
|
4.2
|
|
2102
|
3.9
|
|
2118
|
3.3
|
|
2130
|
3.5
|
Power supply OFF
|
2139
|
1.9
|
|
2151
|
0.9
|
|
2208
|
0.0
|
|
2221
|
-0.1
|
|
2232
|
-0.
|
|
2244
|
-0.3
|
|
Final Temperature offset between the two
probes: 0.0C
The air temperature was about 4C throughout
the test, and the humidity was quite high, about 80
percent.
Both the plate and the surround were
isothermal during the test; no significant gradients were present in
the IR signals from either.
The data from the images during this period
is as follows:
09_1846.fit sur,sig= 2669.5 8.8
plat,sig= 2682.6 7.9 P-S= 13.1 DT= 0.8
09_1852.fit sur,sig= 2666.5 8.0
plat,sig= 2679.2 8.1 P-S= 12.7 DT= 0.6
09_1902.fit sur,sig= 2662.3 8.6
plat,sig= 2671.7 7.4 P-S= 9.4 DT= 0.1
09_1912.fit sur,sig= 2659.9 8.4
plat,sig= 2669.5 8.1 P-S= 9.6 DT= 0.0
09_1933.fit sur,sig= 2657.1 8.1
plat,sig= 2664.6 7.7 P-S= 7.6 DT= 0.2
09_1938.fit sur,sig= 2655.5 8.1
plat,sig= 2662.6 7.7 P-S= 7.1 DT= 0.3
09_1953.fit sur,sig= 2658.5 8.1
plat,sig= 2717.9 7.4 P-S= 59.4 DT= 1.7
09_2003.fit sur,sig= 2662.3 8.6
plat,sig= 2741.4 8.3 P-S= 79.1 DT= 2.9
09_2013.fit sur,sig= 2665.4 8.8
plat,sig= 2756.2 8.1 P-S= 90.7 DT= 3.5
09_2024.fit sur,sig= 2665.6 8.7
plat,sig= 2763.9 7.9 P-S= 98.3 DT= 3.8
09_2034.fit sur,sig= 2664.6 9.0
plat,sig= 2765.3 8.4 P-S= 100.8 DT= 4.1
09_2044.fit sur,sig= 2661.6 9.3
plat,sig= 2765.7 8.3 P-S= 104.1 DT= 4.2
09_2054.fit sur,sig= 2664.0 9.4
plat,sig= 2757.6 8.8 P-S= 93.6 DT= 4.2
09_2104.fit sur,sig= 2662.6 8.3
plat,sig= 2749.3 7.4 P-S= 86.7 DT= 3.9
09_2120.fit sur,sig= 2653.0 9.4
plat,sig= 2737.0 9.1 P-S= 83.9 DT= 3.3
09_2130.fit sur,sig= 2652.4 9.5
plat,sig= 2730.1 8.5 P-S= 77.7 DT= 3.5
09_2140.fit sur,sig= 2649.0 8.5
plat,sig= 2689.4 8.1 P-S= 40.4 DT= 1.9
09_2150.fit sur,sig= 2644.8 8.4
plat,sig= 2665.8 7.8 P-S= 21.1 DT= 0.9
09_2206.fit sur,sig= 2640.1 8.4
plat,sig= 2650.6 8.1 P-S= 10.5 DT= 0.0
09_2221.fit sur,sig= 2639.8 7.8
plat,sig= 2648.3 7.3 P-S= 8.6 DT= 0.1
09_2241.fit sur,sig= 2641.6 7.9
plat,sig= 2647.6 7.6 P-S= 6.0 DT= -0.3
The filenames are date_mst.fit; sur,sig
give the mean level in the image of the surround and the noise.
plat,sig give the mean level in the heated plate and its noise. The
heater power supply was turned on about 19:40 and off about 21:30. A
crude least-squares fit to these data yield the relation
DADU = 5 + 23*DT
The zero point doubless reflects some lack
of perfect flatness in the camera system response, but amounts to an
error of only about 0.2C. The data indicate that there is a
significant lag between the IR level and the measured temperature,
but this has negligible effect on the results.
Some herringbone-pattern coherent noise is
evident in the frames with high sigma; the frames with sigma about
7.5 ADU appear clean. This sigma appears to be the noise floor, and
represents the noise also on clean sky. It appears to be accurately
gaussian.
The night was cloudy, and the sky level
measured after the tester was removed was about 1700 ADU. The
detector is a photoconductor and should have a signal proportional to
the flux, but lacking details of the filter and the device response
we will assume that the response is linear in the temperature; this
is certainly satisfactory for the plate, but is approximately correct
for the sky as well. Thus on this night, the sky was 2660 - 1700 DN
-> 42C colder than the plate/air temperature, or about -38C. It is
doubtless MUCH colder than this on a dry night. The noise represents
about 7.5/23 = 0.33 C/pixel. The image of the heated plate is 70
pixels square, so the pixels are (0.5 deg)^2 as advertised. Thus the
equivalent noise temperature per square degree is about 0.16 C-deg on
5 minute centers, or 0.35 C-deg-(min)^0.5
Doug Finkbeiner notes that the present
system is much too slow to follow small cloud features, and suggests
a cycle time at least a factor of 5 faster, yielding an image per
minute. Given the extremely crude cold baffling of the current system
(the effective stop and the filter are both at room temperature) this
should be easy to achieve with sensitivity equal to the much slower
current system. He also points out that 0.5 degree is much higher
resolution than is required. I therefore suggest that we place as
requirements on a new system something like the following:
FOV: 140 degrees, circular
cycle time: < 1 minute
dynamic range: 0-330K
flux resolution: >= 12 bits, so 1 bit
< 0.1K near room temperature
noise: < 0.16
C-deg-(min)^0.5
histogram sampling: better than 1
ADU/sigma. Note that this may require better than 12 bit sampling.
If the system has angular resolution of 1 deg, 1 minute cycle, and
the minimum noise spec, the noise is 0.16C -> (0.16/330)*4096 =
2 ADU, but if the noise is more than a factor of two better, a
14-bit converter will be required.
angular resolution: < 1
deg
flat-field stability: < 0.2C p-p over
one-hour timescales