Part 1
Open and inspect the files in the following subfolders within one of your User Observations folders:
- Raw Images: This is the preprocessed image, fresh off the telescope with no calibrations applied
- Master Calibration Images: These are calibration images taken to remove sources of noise in the raw image. There are three calibration images, corresponding to three different corrections made to the data:
- bias image (or frame) corrects for readout noise, from the read-out amplifier electronics
- dark frame corrects for thermal electrons in the image, i.e. counts that don’t come from photons but from thermal electrons in the conduction band
- flat field corrects for differences in sensitivity across the chip, including obscuration by dust particles which look like big donuts because they’re out of focus!
- Reduced Images: This is the final version of the image, processed for you by the Skynet algorithms, using the calibration images
What is the typical pixel count in the bias frame?
In the dark frame?
In the flat field?
What is the exposure time of the bias frame?
The dark frame?
The flat field?
What is the exposure time of the raw image frame?
Part 2
How to correct for the bias, dark, and flat using image math.
Image math refers to the fact that we can perform operations on the digital images on a pixel-by-pixel basis. The two basic types of operation we’ll work with are:
- Scalar: A scalar operation is applied to each pixel in an image, e.g. every pixel value is divided by 10 in the resulting image
- Image: Two images are involved in the operation, pixel-by-pixel, e.g. Image1 + Image2 means that the value of pixel (1,1) in the resulting image is the value of pixel (1,1) of Image1 plus the value of pixel (1,1) in Image2 (same for pixel (1,2), pixel (1,3) and all other pixels)
For our image reduction we want:
Final image = [Raw_image – bias_frame – (dark_frame)(exposure_time_scale) ] / (normalized_flat_field_frame)
where
- exposure_time_scale is a scalar = (exposure time of raw image)/(exposure time of dark current frame)
- normalized_flat_field_frame is an image = flat_field_frame/(mode of flat field frame)
recall that the mode of a set of numbers (pixel values here) is the most frequently occurring value
Step-by-step instructions
- Subtract the bias frame from the raw image using the Pixel Operations tool in Image mode.
- Check the exposure times of the raw images and the dark frame to determine if you need to scale the dark frame to match the exposure time of the raw data. If your dark frame is the same exposure time as your raw data, you do not need to scale it. If it is a different exposure time than your raw data, perform the scaling by multiplying the dark frame by the appropriate factor using the Pixel Operations tool in Scalar mode.
- Subtract the (scaled if necessary) dark frame from the result of step #1 using the Pixel Operations tool in Image mode.
- Inspect the flat field frame using the Display tool to determine the mode, the pixel value that is most common.
- Divide the flat field frame by the mode value using the Pixel Operations tool (Scalar mode).
- Divide the result of step #3 by the result of step #5 using the Pixel Operations tool in Image mode to get the Final image.
More information: A basic First-Aid procedure for CCD images
Compare this Final image (your reduced image) to the Skynet reduced image, either by subtracting the two or by dividing them. Describe which operation you did and what the resulting image looks like. What is the range of pixel values in the resulting image?
Submit:
- your Final image
- the Skynet reduced image
- your answers to the questions posed above (in red).