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Image Acquisition and Reduction - by Ricky Leon Murphy:

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Image Acquisition and Reduction:

Our software of choice will be MaxImDL. This software is capable of controlling a CCD camera, and performs some very powerful image edit using a wide variety of tools. With this software, we are able to remove any bad or damaged pixels, calibrate images using the bias and flat frames, create a color composite, and perform photometry – which is the only method used to create the data points on a CMD. In addition to MaxImDL, we will also use Microsoft Excel to store the photometric data as well as perform image calibration and create the actual diagram. The image reduction process itself is a fairly simple concept, but time consuming. While the attached appendix documents every step of the reduction process, a guided tour is provided:

Each image has, in addition to the actual image (or flat or bias), an area of unexposed
pixels used to store bias information. This area is called ‘overscan.’ Because we are calibrating various images using various filters, the overscan area is of no use and must be removed. There is a thin column of nothing on the far right of the image on the left. This thin area is the overscan and is present in every image provided by the McDonald Observatory. This area can be mapped out within MaxImDL and applied to every image. In addition to

the overscan, the border of the entire image, which is one pixel in width, must also be removed. This has been included in the map.

The remove bad pixels tool under the process menu is able to remember the selected pixels in one image. The unfortunate is that every pixel has to be selected by the mouse, or entered by hand – if you know the exact pixel location. In this case, I renamed my map1 to Remove Overscan – SAO project. To apply this map to other images, I open this tool and click the process button.


The result of the overscan removal is seen here. Notice the dark area on the far right is no longer present. This image of M67 is oriented properly versus the mirrored image above. Once the overscan has been removed from all cluster images, standard fields, bias and flat images, the headers of each image must be fixed.


Looking at the view menu within MaxImDL, there is a tool to view the fits header. Every image captured on professional CCD cameras store important information about the image captured. For example, the size of the chip, date of image capture, duration of image capture, the airmass value, and filter used is recorded in this header. Also, demographic, telescope type, and astrometry information can also be a part of this header. Within this header, the areas of overscan have been removed, and the image size has been updated to reflect the actual image size (details are available in the image reduction appendix). In the image above, the value ‘airmass’ is highlighted. This value is an important one as it is used to determine the airmass value of the atmosphere. Every night, the quality of the atmosphere (called the “seeing”) is given a numerical value. This value also changes as the area photographed is closer to the horizon. The airmass values included in the fits header will probably be incorrect. The good news is there are airmass calculators available so the correct airmass value can be found. The bias and flat field images do not require a correct airmass value as no object is being photographed through the atmosphere.

Once the overscan has been removed and the fits headers correct, image calibration can begin. As above, there is a detailed report of image reduction in the image reduction diary; however, a brief tour will be give.

Bias and flat images are more effective of there are more of them. These files are combined into a master file. The first file to create is the bias.

As you can see, there is nothing fancy about a bias image. The sole purpose is to create a calibration as to the levels of brightness for every image the bias has been applied. On close inspection, there are very small white dots on this image. These are not a part of the bias measurement and will be averaged out when combined with other bias frames.


Combining files is a two step process. The first step is to locate the images you wish to combine using the combine tool under the file menu. The more images the better, but in our case we have 5 (only for are selected here, but all five images were selected to create our bias frame.


When the combine button is clicked, the second step of the process is available. Auto – star matching is selected automatically, but will be ignored in this case since there are no stars to match. It is important to select median under the output option. This averages the information in all the images and combines them to a single master image. This is the process that removed the white dot artifacts on the individual bias images. This process of combining files to a master file will work for both the bias and the flat images. Due to the purpose of the star cluster images, the science images are not to be combined!

The result of the combine is an equally boring, but important image. In addition to combing the bias frames, the flat frames will also be combined. Since we are using BRI filters for our analysis, flat frames for filters BRI are required; however, before any flat frames are combined, the bias image must be applied to the flat fields.

The calibration tool, under the process menu has two variants: set calibration or calibration wizard.


Version 4 of MaxImDL has a very capable calibration wizard that I highly suggest. The wizard walks you through the entire process. The image on the left shows the calibration setting for the science images, but the calibration tool can also use only the bias image to calibrate the flat fields. Once the master bias file is selected, simply open all of the flat frames and select calibrate all under the process menu.  This method will also be used for the science images.


The image on the left shows a single flat image (from the I flat field) and the image on the right is a median combine of 5 flat images. This image will be used to calibrate all of the science images using the I filter. The red and the blue flat images look similar to the images above, and will not be demonstrated to save space.

Using the same calibration above, apply the calibration to the appropriate images. Every image will use the master bias frame, but the filter specific images will require the master file counterpart – i.e. images through the B filter must be calibrated with the master bias and the master B flat.

The result is a nicely calibrated science image with no gross defects.

Now that we have calibrated images, we can perform photometry on all the images. For the purpose of calibration to the Landolt system, we will use images of globular cluster NGC4147, Landolt Standard Area 104 (SA104), and Landolt Standard Area 107 (SA107). As mentioned above, airmass plays a role in a telescopes ability to ‘see’ a star. This affect can also interfere with the color term. As a result, the fields NGC4147, SA104 and SA107 will be images at various times through the night, so the position of these areas will cause a difference in airmass value. These values, shown later, will affect the outcome of the color term. The apparent magnitude of selected stars in each of the calibration fields will need to be documented. The best method is to use the photometry tool within MaxImDL.

The photometry and information windows work in tandem. In order to create a photometry plot, a reference star is to be selected. A single reference star from the Landolt standards can be selected, and the magnitude of the reference star is required for the Ref Mag field. The remaining stars are selected as objects. When the plot is viewed, the results can be saved as a CSV (comma separated values) file.

To the left is a portion of an image being analyzed for photometry. The Ref1 is the reference star, and the Obj1, Obj2…... are the object stars. Once the photometry plots are saved, the data can be entered in an Excel spreadsheet. The attached spreadsheet contains all of the instrument magnitudes of each filter (the photometric data) as well as the Landolt standards.


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