The jellyfish nebula is a notoriously dim object in the night sky. As a result, most images of this nebula are highly denoised, leading to a loss of detail. But by combining nearly 1000 exposures and 6740 minutes of exposure from collaborators across three continents for a total integration time of 112.4 hours, we were able to reveal structures and detail previously not displayed by previous images.
In this image, channels are mapped in the classic Hubble palette, where ionized sulfur is represented by red, ionized hydrogen is represented by green, and ionized oxygen is represented by blue.
The Jellyfish Nebula is a stupendously complicated nebula created by the remains of a massive star that exploded. In the center of this jellyfish lies interweaving tendrils of hydrogen, sulfur, and wispy oxygen warped by an intragalactic wind coming from the northeast. Dark lanes of inert dust obscure small portions of the Jellyfish. The bowshock-like “head” of the jellyfish was most likely formed by its contact with the denser nebulosity to the left of the jellyfish. The Sulfur and Oxygen tendrils escaping the Jellyfish to the left and to the bottom are most likely a result of unstable magnetic fields from the supernova. The field of nebulosity to the right, although unrelated to the supernova, greatly affects the movement of the supernova’s western region.
Image:
Starless image:
Credits:
Acquisition: Andy Brown (@abastrophotouk), Dominic (@domnuch), Jay Aigner (@aignerastro), Oliver Carter (@bright_ascension), Tommy Lease (@colorado_astro), Jens Unger (@jazz.astro), Sendhil (@deepskyimaging), Justin P. (@justadudewitha_camera_)
Image processing: William Ostling (@the_astronomy_enthusiast), Justin P. (@justadudewitha_camera_)
Data details:
Ha: 2750 minutes, 389 subframes
Sii: 1930 minutes, 271 subframes
Oiii: 2060 minutes, 277 subframes
Processing:
Pre-processing and stacking
- All subframes were weighted in subframe selector based on the following formula:
(30*((PSFSignalWeight-PSFSignalWeightMin)/(PSFSignalWeightMax-PSFSignalWeightMin)) + 18*(1-(Eccentricity-EccentricityMin)/(EccentricityMax-EccentricityMin)))
+ 18*(1-(FWHM-FWHMMin)/(FWHMMax-FWHMMin))
+ 14 *(SNRWeight-SNRWeightMin)/(SNRWeightMax-SNRWeightMin)
+ 20
- Each subframe was star aligned with distortion correction. Some subframes were cropped to aid the star alignment
- Subframes were integrated using the linear fit clipping rejection algorithm
- Subframes were drizzled with a scale of 1 to remove star alignment artifacts from the final master images
Preparation of all frames:
- each channel was star aligned to H-alpha with distortion correction on
- Several iterations of DBE were applied to remove gradients
Creating a luminance
- Each master was stretched using the default STF settings
- the following pixelmath formula was applied: (Ha + max(Ha, Sii) + max(Ha, Oiii))/3
- A re-linearization was applied with a midtones level of .999367
Deconvolution of luminance
- A PSF was created using the dynamic PSF process
- A starmask was created using Starnet 2, morphological transformation, and convolution
- Deconvolution was applied with local deringing
Noise reduction (Linear)
- A low-contrast mask was applied
- Two iterations of TGV noise reduction were applied, one targeting high-frequency noise and one targeting low-frequency noise
- A medium-contrast mask was applied
- MMT targeting all 8 scales was applied to remove large-scale noise
Stretching
- due to the High-contrast nature of the nebula, the masters were stretched using Masked Stretch
- a histogram transformation with a 0% black clip was applied
- Masked stretch with 1000 iterations, a black level of .1 was applied to each master
Channel combination
- The SHO channels were combined using channel combination
- The resulting image was inverted and SCNR was applied
- Starnet 2 was applied to the image
- LRGB combination using the master luminance was applied
Non-linear adjustments
- Star reduction using Adam Block's method
- Histogram channels were matched using histogram transformation
- Overall brightness adjustments using curves
- Color adjustments using curves and color masks
- Using a green color mask, green levels were brought down and red levels were brought up
- Using a red color mask, the hue was shifted slightly towards red
- Using a blue color masks, blue and green levels were increased and red levels were decreased
- Saturation adjustments
- The luminance and SV components were extracted
- The pixelmath formula L * ~SV was applied to create a saturation mask
- saturation values were increased using the mask
- Iterative color and saturation adjustments were applied
- Non-linear noise reduction
- A mask was created to target background areas
- MLT was applied targeting 5 layers of luminance
- MMT was applied targeting 4 layers of chrominance
- More brightness adjustments were applied using curves transformation