What is Messier 92?
Messier 92 (M92) is a bright globular cluster located in the constellation Hercules. It is one of the oldest known globular clusters, with an estimated age of approximately 14 billion years. This impressive cluster has a diameter of about 109 light years and is surrounded by a dense collection of stars, resembling the nucleus of a large comet.
A globular cluster is a spherical collection of stars that are gravitationally bound together. These clusters typically contain hundreds of thousands to millions of stars, which are densely packed in their cores. Messier 92 is particularly notable for its brightness, with an apparent magnitude of 6.4, which is only a little less bright than Messier 13 which is also located in the constellation, Hercules.
Located northeast of the bright star Beta Herculis, M92 can be easily found in the night sky. It was discovered independently in 1777 by Charles Messier and has since been extensively studied. When observed through a telescope, particularly with a large reflector or a refractor, M92 reveals a clear and brilliant sight of the stars within the cluster.
As one of the objects listed in the Messier catalog, Messier 92 is a popular astrophotography target for both amateur and professional astronomers. The cluster’s distinct features make it an impressive sight, and photographs of M92 often showcase its densely packed core and the surrounding nebulosity.
I personally love this globular cluster. In fact, I think it’s beauty tops that of the king of globular clusters, M13, which I recently took as well.
How I took this
Below is information on how I took this including my equipment and integration time.
Equipment
I used the following equipment to take this:
- Celestron Edge HD 8″ Telescope
- ASI533MM Mono Camera
- Antlia LRGB-V Color Filters
- ZWO AM5 Mount
- ASIAir Plus
Integration Time
This image had a total of 4 hours of integration time which roughly worked out to about an hour for each filter: luminance, red, green, and blue.
Processing Techniques
I processed this in Pixinsight. As many astrophotographers know, Pixinsight is the standard app for processing astrophotography. I am breaking down my steps into 2 different categories: (1) Basic, which can be found in my Pixinsight Processing Video Playlist & Unique steps which are steps that are somewhat unique to globular clusters.
Basic Pixinsight Steps
- Dynamic Crop
- Dynamic Background Extraction (DBE)
- Deconvolution (using Blur Xterminator)
- Noise Reduction (using Noise Xterminator)
- Channel Combination to combine the R, G, and B images and make a color image
Unique Pixinsight Steps for Globular Clusters
GHS (Generalized Hyperbolic Stretch)
Stretch the following images using GHS. Globular Clusters are a good fit for GHS. If you don’t have GHS installed already, you can get a link to the repository here for Pixinsight.
- RGB
- Luminance
LRGB Combination – Add Luminance
Make any adjustments to the 2 images so they are about the same exposure level. It won’t be good to have to have a very light background and the other to have an extremely dark background. Use Curves Transformation to even them out.
RGB and luminance before LRGB Combination. RGB is on the left and luminance is on the right. Because there was only 1 hour of luminance here, it’s not going to be so dramatic when I add it in to the RGB image.
The image after luminance is added in via LRGB Combination. It’s not that dramatic of a difference, but there definitely is a bit more detail.
Star Xterminator
Next we are going to split the image by removing the stars using Star Xterminator. There are many tools out there to do star masks, but I love Star Xterminator. It’s not for everybody since it’s a paid plugin. When you run Star Xterminator, make sure unscreen stars is checked.
Once Star Xterminator is done, we are left with 2 images:
The first image is the original RGB image with just the globular cluster core remaining, the second image (on the right) is the extracted stars with a deep black background.
Original RGB Image Modifications
First, I am going to work on the original RGB image which is just the background and the core. The goal is to darken the background and to use 2 simple steps:
Use the GAME script to create a mask for the core of the globular cluster. You don’t want to lose any brightness in the core when you darken the image.
Once the mask is in place for the globular cluster core, use Curves Transformation to darken the image.
Extracted Stars Image Modifications
Now, we are going to move on to the extracted stars image and make a number of modifications here to enhance the star’s brightness and saturation. Before we do that, I like to run a Pixelmath Process created by James Lamb which is called Modified SCNR. This is similar to Pixinsight’s own SCNR, but it is more diligent about keeping signal. If you are not familiar with what SCNR does, it removes any green or magenta cast from your image. The original SCNR does the same thing, but it also takes a bit of the signal with it. The modified SCNR Pixelmath helps preserve signal. Once you’ve created the Modified SCNR script, take these steps:
- Run Modified SCNR on the extracted stars (remove green)
- Invert the image
- Run Modified SCNR on the inverted extracted stars (remove magenta)
- Invert the image back to its normal state.
While Modified SCNR and SCNR are mainly used to remove green from SHO images, I find it useful for LRGB images as well. There is always a green or purple/magenta cast. When I created the image without Modified SCNR, there was a dramatic difference in color. This image had a lot of purplish hues and I don’t like purple. No offense Prince, it just has no place in space in my opinion.
Next up, we will do 2 simple curves transformations:
First up is a curves transformation using RGB/K to increase the star’s exposure (or make them brighter).
After we make the stars brighter using curves transformation, I then want to increase the saturation to make the stars more colorful. Notice the color difference between this one and the previous image which had no Modified SCNR applied. No purple here!
Our last step on the extracted stars image is to sharpen the stars. We can do this with local histogram equalization. Here are my settings for local histrogram equalization:
- Kernel Radius = 100
- Contrast Limit = 1.3
- Amount = 0.500
Recombine RGB & RGB Stars
Ok, finally at the end here. We now want to recombine the RGB image with the RGB_Stars image. To do this, we will use a special Pixelmath formula since we clicked “unscreen stars” in Star Xterminator:
combine(rgb, rgb_stars, op_screen())
It looks much nicer on the original background, right?
Increase Saturation
Lastly, I felt the image still needed more oomph. So, I added some more saturation.
That’s it! I hope this helps you in processing globular clusters. While there are so many different techniques and opinions on how to do this–this has worked best for me.
Video: M92 Under Pressure: Imaging a Globular Cluster on a Hot Summer Night
This was my first time making a video which shows my approach from capturing subs outside with the ASIAir to processing them in Pixinsight.
Since I shot this video, I’ve discovered, what I think, are better techniques for processing globular clusters which are mentioned in this post. So, take the processing part in the video with a grain of salt; I think the technique I outline here in this post is much better.
Clear Skies!
