M37 Open Cluster

M37Click image for full size version

February 6, 2019

M37 is one of three bright open clusters in Auriga. The others are M36 and M38. They are great in binoculars or a small telescope, and can all be seen with the naked eye from a dark site. They were first recorded by an Italian astronomer, Giovanni Battista Hodierna before 1654, and are now in the famous Messier Catalogue of dep-sky objects.  M37 contains more than 500 stars with a total mass more than 1,500 times that of the Sun.  It’s several hundred million years old, and is about 4,500 light years away.  From its apparent size in the sky and this distance estimate, the cluster has been estimated to span 20-25 light years. Its triangular shape is distinct in binoculars or telescopes.

I have shot this object before, and hadn’t planned to shoot it at this time. However, I needed to diagnose an electrical noise issue affecting my images. When a short opportunity to test a new USB cable appeared, I took it. Acquisition time for this image is a paltry 25m!

My 2010 image was captured with a similar-sized setup (62m, 6″ f/8 refractor and QSI583wsg camera on MI-250 mount). My 2016 image was taken with a larger telescope (2hr25m, ASA 10″ with SBIG STL-11000 camera).


Sky-Watcher Esprit 150 f/7 refractor, QHY 16200-A camera, Optolong R, G and B filters, Paramount MX. Acquisition with TheSkyX unguided. Focused manually with Bahtinov mask. Automation with CCDCommander. All pre-processing and processing in PixInsight. Acquired from my SkyShed in Guelph. No moonlight, average transparency and average seeing. Data acquired December 18, 2018.

1x5m R, 2x5m G and 2x5m B (Total = 25m).
Image scale is 1.15″ per pixel

Data Reduction and Cleanup
The BatchPreProcessing script was used to perform calibration, cosmetic correction and registration of all frames. PixelMath was used to make the G and B masters (each was an average of two frames)and the single R frame was used as the Red master. The R, G and B images were cropped identically with DynamicCrop. DynamicBackgroundExtraction was applied to each master. 

RGB Creation and Processing
Creation and cleanup: ChannelCombination was used to make a colour image from the R, G and B masters. The RGB image was processed with PhotometricColorCalibration using a small preview of background sky as the background reference.  

Linear Noise Reduction:  MultiscaleLinearTransform was used to reduce noise in the RGB image. An external mask was used, with layer settings for threshold and strength as follows: Layer 1: 3, 0.85   Layer 2: 2, 0.7  Layer 3: 2, 0.5  Layer 4: 1, 0.2.

Stretching:  HistogramTransformation was applied to the RGB image to make a pleasing, bright image. 

Synthetic Luminance
Creation and cleanup of SynthL: The registered, calibrated and cosmetically corrected subs from all channels were integrated ImageIntegration (average, additive with scaling, noise evaluation, iterative K-sigma / biweight midvariance, Percentile Clipping rejection).

Deconvolution:  A star mask was made to use as a Local Deringing Support image. A copy of the image was stretched to use as a range mask. Deconvolution was applied (50 iterations, regularized Richardson-Lucy, external PSF made using PSFImage script tool with 30 stars).

Linear Noise Reduction: MultiscaleLinearTransform was used to reduce noise in the SynthL image. An external mask was used, with layer settings for threshold and strength as follows: Layer 1: 3, 0.75   Layer 2: 2, 0.6  Layer 3: 1, 0.4  Layer 4: 1, 0.1.

Stretching:  HistogramTransformation was applied to the SynthL to make a pleasing, bright image. 

Combining SynthL and RGB
The processed SynthL was applied to the RGB image using LRGBCombination.

Additional Processing

Nonlinear Noise Reduction: TGVDenoise was used in L*a*b* mode to reduce noise in the background using the same mask used for linear noise reduction.

Final Steps: Background and star brightness, contrast and saturation were adjusted in several iterations using CurvesTransformation with masks as required. CloneStamp was used to remove hot pixels.