PROCESSING
APY Masterclass: Framing a dark molecular cloud
How this image of NGC 6729 and cluster NGC 6723 was created
The juxtaposition of objects in an astrophoto can make an enticing composition. I used this idea as the basis for my image of the dark molecular cloud NGC 6729 in Corona Australis and globular cluster NGC 6723, just 15 arcminutes distant in Sagittarius, which was shortlisted in the Astronomy Photographer of the Year 2021 competition. Here, I’ll explain the steps that helped me create this image.
If you are planning an image like this, the free sky atlas Aladin (bit.ly/3x5ekhN) allows you to simulate your main and guide camera’s field of view and apply these to provide the desired layout and optimal guide star placement. This is the first step in understanding how your image will look, plus it provides you with the precise astronomical coordinates for the object and camera rotation in degrees.
At this point, I find it helpful to assess the nature of the objects in the field of view and see whether any special filters are required. For NGC 6729, Hydrogenalpha (Ha) was going to be important to highlight the faint, deep red nebulous colours, while for the natural colours, Red, Green and Blue filters were used. Next, Luminance and Ha were used for the final resolution.
Once the data is collected, the image-processing program CCDStack (bit.ly/3JeNnus) can be used to help with the heavy lifting of calibration. The steps are straightforward and it only takes a few minutes to process to your master frames per filter. There are two steps to highlight in the calibration process, which I think make the major difference between ending up with an okay image and producing a great one that stands out.
Removing unwanted artefacts
Firstly, astronomical cameras can have hardware issues in the form of either column defects, bad pixels or hot spots, which effectively leaves gaps in the captured data. You can tackle these issues by dithering – slightly moving the telescope – between the captured photos. This means you can capture the missing detail in your sub-exposures. So, after applying flats, darks and bias frames to each sub-exposure, it’s important to neutralise any of these hardware defects in the images prior to combining all the sub-exposures together.
In CCDStack, this function is found under ‘Process Repair Pixels/Columns’ (see Screenshot 1). If you then create a Repair Map, by clicking the button ‘Create Map’, it highlights in red all the defects within the field. Upon applying the tool, it treats each subexposure individually to fill in these missing regions, and once applied it provides you a lovely set of clean images, free of hardware defects.
After all the images are aligned, the second most important step is the elimination of any remaining artefacts, for example satellite trails and cosmic rays impacting on the imaging chip while imaging. These items show up randomly and can be removed using their randomness to select them. Under the menu item ‘Stack > Data Reject’ (see Screenshot 2), CCDStack has a large selection of rejection options that can be used accumulatively to tag remaining image defects. What is particularly good is the reported level of rejection in a selected area, so you can review that for each sub-exposure.
After producing each ‘master frame’ in Red, Green, Blue, Luminance and Hydrogen-alpha, I used both CCDStack and PixInsight (https://pixinsight.com/) to combine the data to see which program provided the most suitable, colour-accurate result. Each package has its specific abilities to combine the masters, and I love fully processing images with both programs and then choosing my preferred one.
It is important to remain broadly focused on all the tools within each product, especially in PixInsight, which has powerful processes and scripts to help combine complex colour schemes. This includes those in the batch-processing area that need to be learned to be appreciated: these can help introduce effects that are not easily achieved outside PixInsight. For setting the white balance of the image, I use PixInsight’s ‘PhotometricColorCalibration’ (PCC) process (see Screenshot 3).
An important factor when processing this image was the introduction of Hydrogen-alpha data to highlight nebulous regions. PixInsight’s Utility Script called ‘Emmission Line Integration’ (see Screenshot 4) is perfect for this task, as it allows you to introduce narrowband data to a specific colour channel in a broadband image, in this case Red. This tool enables you to add up to three different narrowband filters to the RGB master, with an ‘Amplify’ input box that allows you to select the desired strength per filter.
To add finishing touches to an image, I like to combine the final colour master to the highresolution Luminance and Hydrogen-alpha masters in Photoshop, which I did here. As you can see, there are great products at your disposal to help make your final image your own masterpiece.
3 QUICK TIPS
1. Plan the target’s layout in accordance to your field of view before setting up at your favourite dark-sky site.
2. Correctly calibrate the data to remove imaging chip hardware defects and randomly occurring defects like satellite trails and cosmic rays.
3. Combine narrowband highlights into the broadband colour channels to bring out special features.
Steven Mohr is an astrophotographer based in Melbourne, Australia.
He was shortlisted in APY 2021 for his image ‘Dark Molecular Cloud in Corona Australis’