## Info

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Figure 8.1 It takes only five images to determine your camera's conversion factor and readout noise. You can obtain a set of images like these without taking your CCD camera off the telescope during a regular observing session. Checking your camera assures that you are getting the performance you expect.

8.2.2.1 Step 1: Mean and Standard Deviation in the Bias

Load the two bias frames, BCT-BF1 and BCT-BF2. Add them together and measure the mean value of a region near the center of the image. The size of the region is not critical; an area 100 pixels on a side is entirely adequate. The mean value is the quantity B\ + B2 . (The notation B is read as "B-bar," and the bar over the B says that it's a mean value.)

Next, subtract one bias frame from the other, and measure the standard deviation in pixel value for the same region at the center of the frame. Subtracting the two bias frames removes any fixed bias patterns, leaving just the noise from the two bias frames, <5bx-b2 > which is Jl times the noise of one bias frame.

• Tip: In AIP4Win, you can add and subtract images using the Image Math tool. Perform the measurements with the Pixel Tool.

8.2.2.2 Step 2: Mean and Standard Deviation of the Flats

Load the two flat frames BCT-FF1 and BCT-FF2. Add them together and then measure the mean value of a region near the center of the image. The result is

Next, subtract one frame from the other, and measure the standard deviation in pixel value for a region near the center of the frame. Subtracting the two flat Figure 8.2 The difference between two seemingly identical flat-field frames is noise—in this case, the random variation in the number of electrons generated at each photosite. This image has been stretched by a factor of 20 relative to the two flat-frames, shown in Figure 8.1, that were subtracted to create it.

Figure 8.2 The difference between two seemingly identical flat-field frames is noise—in this case, the random variation in the number of electrons generated at each photosite. This image has been stretched by a factor of 20 relative to the two flat-frames, shown in Figure 8.1, that were subtracted to create it.

frames removes any features in the flat, leaving the total noise in the two flat frames, gf _f , and the noise you have measured is 72 times the noise in a single flat frame.

•Tip: In AIP4Win, you can add and subtract images using the Image Math tool. Perform the measurements with the Pixel Tool.

8.2.2.3 Step 3: Measure the Dark Current

Load the dark frame, BCT-DF, and bias frame BCT-BF1. Subtract the bias frame from the dark frame. Measure the mean pixel value for a region at the center of the frame. This is the dark current, £>Adus > that accumulated during a 60-second integration, measured in ADUs.

• Tip: In AIP4Win, you can add and subtract images using the Image Math tool. Perform the measurements with the Pixel Tool.

8.2.2.4 Step 4: Compute the Conversion Factor

You have measured the mean of a high signal level and its standard deviation in ADUs. Because you expect the signal to display Poisson statistics measured in electrons, you expect electrons= jFeiectrons. However, since both o and F have been multiplied by the conversion factor, g electrons per ADU, you have actually measured gaelectrons= . Solving then for g:

CT electrons

In fact, you have done an even better job because you have measured the bias mean and noise, so you can remove their influence by subtracting them. To compute the conversion factor, evaluate:

In 16-bit cameras, the conversion factor is usually close to unity.

8.2.2.5 Step 5: Compute the Readout Noise

The only source of noise in a bias frame should be the readout noise. You have measured the sum of readout noise in two bias frames. To compute the readout noise, evaluate: 