Recommended Deep Sky Stacker settings

Deep Sky Stacker (FREE) remains a one of the most popular pre-processing applications for Windows. Stacking and saving your data with these settings is essential to getting good results from StarTools.

When applying the important pre-processing do's and don'ts when using StarTools with any stacker, you will want to configure Deep Sky Stacker specifically in the following manner.

• Choose No White Balance Processing in the RAW/FITS dialog
  
• Choose Bilinear Interpolation for the Bayer Matrix Transformation algorithm
  
• Save your final stack as 32-bit/channel integer FITS files, with adjustments not applied.
• Stack with Intersection mode - this reduces (but may not completely eliminate) stacking artifacts
• Do not choose Drizzling, unless you are 100% sure your that; your dataset is undersampled, you have shot many frames, and you dithered at the sub-pixel level between every frame
  
• Turn off any sort of Background Calibration
• Some users have reported that they need to check the 'Set black point to 0' checkbox in the 'RAW/FITS Digital Development Process Settings' dialog to get any workable image.
• Choose Kappa Sigma rejection if you have more than ~20 frames, use Median if you have fewer.
• Ensure hot pixel removal is not selected on the Cosmetics tab

With all the above settings made, you can then safely stack and (assuming you used a DSLR or OSC) import your dataset into StarTools as "Linear, from OSC/DSLR with Bayer matrix and not white balanced".

If stacking multiple mono datasets for use in a composite, make sure to use one set's finished stack as a reference to stack the others with; StarTools's Compose module requires every dataset to be the same dimensions. Aligning remaining channels against an initial channel during stacking is particularly important to ensure consistency of point spread functions across channels; do not align finished stacks against each other after stacking.

Please consult the "Important dataset preparation do's and don'ts" section for further advice on improving your datasets.