LBN777 and M45 in Taurus

The Happy Lil' Ghost Nebula (LBN777) to the Pleides (M45)

Full Resolution

After over a year attempting to shoot this region I’ve finally completed a revision of the dark molecular clouds that cover the taurus region near the Pleiades. This is but one panel of a larger 8-panel mosaic that I’ve planned and hope to complete before summer gets here. But this region in particular is one that I am a huge fan of. it is home to the very popular and bright star cluster M45 the Pleiades that sits behind the molecular cloud, but it’s radience shines through the dust. Through the dust structures one can find LBN777, a “ghost like” molecular cloud that to my eyes looks like it’s about to eat that bright yellow nearby star is more commonly known as the Vulture Nebula or Baby Eagle Nebula.

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Equipment:

  • Celestron CGEM Mount

    • Self tuned / hacks to get guiding stable include:

    • Intentional offset polar alignment so dec always pulses in one direction

    • Balance “west” heavy (rather than the recommended east) so that the ota “falls” onto the gear teeth rather than get “lifted”

    • Factor Reset hand-controller daily (to prevent cgem from being possessed and forgetting where the meridian is on subsequent night)

    • Dither in RA only

  • Rokinon 135mm f2 at f2

  • asi071mc PRO at -15 C

  • Astronomiks L3 UV/IR filter

  • Widefield Rig + AF3 by Deep Sky Dad

Acquisition:

  • 123x120s at unity gain (4 hours) taken across four nights in 2019 Dec: 01 - Dec 04. The Moon went from 20%-50% illuminated during these nights but all data taken while moon was below or near the horizon.

Session sequenced using Nighttime Imaging in Astronomy: N.I.N.A

All pre-processing and post-processing was done in PixInsight and final touches added with Affinity photo. Full details below.

The resulting image is a combination of the following steps:

  • Inspected all subs for bad images with Blink, discarding subs containing clouds

  • Calibrated all subs with a master flat and master dark

  • Used subframe selector to weight all images based on the following weighting

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 (10*(1-(FWHM-FWHMMin)/(FWHMMax-FWHMMin))

+ 20*(1-(Eccentricity-EccentricityMin)/(EccentricityMax-EccentricityMin))

+ 30*(1-(Median-MedianMin)/(MedianMax-MedianMin))

+ 10*(SNRWeight-SNRWeightMin)/(SNRWeightMax-SNRWeightMin)

+ 10*(Stars-StarsMin)/(StarsMax-StarsMin))

+ 30

  • Selected the best sub from subframe and blink to use as a reference frame for aligning and integration

  • Cropped the stacking edges of the integrated image

    • Cropped with Unlinked Screen Transfer Function

  • Dynamic Background Extraction

    • Dynamic Background Extraction - Large and Medium Sized Points with medium smoothing factor
    • Dynamic Background Extraction - Results

  • Background Neutralization using 5 small preview windows aggregated as a background reference

  • Color Calibration |> Solved |> Photometric Color Calibration

    • Color Calibrated with Linked Screen Transfer Function

  • RGBWorking Space to 1,1,1

  • Noise Reduction was done using TGV Denoise with a low contrast mask and an autostretched local support and MMT with a very protective luminance mask

    • After applying TGV and MMT Noise Reduction

  • Stretched using Arcsinh Stretch followed by Masked Stretch

    • Initial Stretch of Color Data using Arcsinh Stretch
    • Subsequent Stretch with MaskedStretch

Luminance Processing

  • A synthetic luminance was extracted prior to the stetching of the RGB data for separating luminance processing L

    • Extracted Luminance with Screen Transfer Function

  • Stretched using Masked Stretch and Histogram Transformation

    • Masked Stretch with Histogram

  • Created a Starless version of the luminance using starnet++ and a StarsOnly image by subtracting the starless from the luminance Starless-L

    • Starless Version after applying StarNet++

  • Performed two rounds of Local Histogram Equalization on the starless image

    • Kernel Radius 128 | Contrast 4 | Amount 0.100 | 8-bit | Circular

    • Enhance Contrast of Nebulosity using Local Histogram Equalization (Radius 128)

    • Kernel Radius 256 | Contrast 8 | Amount 0.030 | 8-bit | Circular

    • Further Enhance Contrast of Nebulosity using Local Histogram Equalization (Radius 256)

  • Performed large scale sharpening and noise reduction with Multiscale Linear Transformation 6 levels

    • Larger scale sharpening with Multiscale Linear Transformation

  • Recombined the enhanced Starless with the Stars only image with Pixel Math Starless+StarsOnly

    • Re-add the stars back to the starless

Bringing the Enhanced Details back into the Color Data and Final Steps

  • Used ChannelCombination in CIE L*A*B mode to apply Luminance to Color

    • Re-apply the luminance details back to the color image

  • Stars in this image were reduced by using two rounds of morphological selection with a luminance based contours mask created with the following steps:

    • Extract a new luminance from the color image

    • Apply the following pixel math expression to a new extracted luminance to create a contours version using the StarsOnly image created earlier: StarsOnly/$T-$T

    • Morphological Selection with Contours Mask to reduce star intensity

  • Curves transformation was used to increase contrast and saturation with a luminance mask

    • Shift colors and contrast with the Curves Adjustment tool

  • Over saturated stars were magenta and were fixed by inverting and applying SCNR green before re-inverting

    • Fix the oversaturated Magenta tones with SCNR

  • An ICC Profile was applied to enable Black Point Compensation

  • The image was exported as a 16bit TIFF file and loaded into Affinity Photo

  • Final round of noise reduction was applied with Affinity

  • HSL enhancements were applied to shift the dust colors to emphasize their golden brown color

    • Enhance tones with HSL tool and Selective Color Adjustments in Affinity Photo

  • The final image from Affinity Photo and the final image from PixInsight was blended together to ensure a more natural reduction of noise levels


Constructive criticism is welcome. Let me know what you think! How can I improve?