Calibration of a CCD Camera and Correction of its Images

Charge-Coupled-Device (CCD) cameras have opened a new world in astronomy and other related sciences with their high quantum efficiency, stability, linearity, and easy handling. Nevertheless, there is still noise in raw CCD images and even more noise is added through the image calibration process. This makes it essential to know exactly how the calibration process impacts the noise level in the image. The properties and characteristics of the calibration frames were explored. This was done for bias frames, dark frames and flat-field frames at different temperatures and for different exposure times. At first, it seemed advantageous to scale down a dark frame from a high temperature to the temperature at which the image is taken. However, the different pixel populations have different doubling temperatures. Although the main population could be scaled down accurately, the hot pixel populations could not. A global doubling temperature cannot be used to scale down dark frames taken at one temperature to calibrate the image taken at another temperature. It was discovered that the dark count increased if the chip was exposed to light prior to measurements of the dark count. This increase, denoted as dark offset, is dependent on the time and intensity of the prior exposure of the chip to light. The dark offset decayes with a characteristic time constant of 50 seconds. The cause might be due to storage effects within chip. It was found that the standard procedures for image calibration did not always generate the best and fastest way to process an image with a high signal-to-noise ratio. This was shown for both master dark frames and master flat-field frames. In a real world example, possible night sessions using master frame calibration are explained. Three sessions are discussed in detail concerning the trade-offs in imaging time, memory requirements, calibration time, and noise level. An efficient method for obtaining a noise map of an image was developed, i.e., a method for determining how accurate single pixel values are, by approximating the noise in several different cases