CCDStack Version 2 Tutorial B. Waddington 4/19/2011 CCDStack Take 2 CCDStack Version 2 Tutorial B. Waddington 4/19/2011

Stack-based Rejection Compute statistics on each “column” of pixels in a stack of images…

Stack-based Rejection Basic Concept Estimate the “most likely value” for each column in the stack – typically the “average” value Look at the dispersion of values in that column relative to the average Reject those pixels that look “too far off the mark” Rejection occurs for individual pixels in each image, not the whole image Most of the data rejection techniques are all about defining “too far off the mark” │

Stack-based Rejection Individual pixels in each column are rejected as being outliers…

Rejected Pixels What happens to the pixels that are rejected? They are assigned a “rejected value” and are excluded from subsequent calculations – but the original values are not lost They may need to be given “imputed” values if there are no “good” values to use in their place (e.g. blooms)

Data Rejection Choices – Sigma Clip Computes the average and std. deviation of the pixel stack; keeps only those pixels that lie within a sigma factor of the average Strong noise rejection but needs 10+ images for best results Allows “tuning” based on the sigma factor you specify Can be iterated │

Sample Values in a “Pixel Stack” Bogus values

Rejection by Sigma Clip 3σ 2σ 3-Sigma Clip

Rejection by Sigma Clip 3σ 2σ 2-Sigma Clip 2-Sigma

Poisson Data Reject Uses a theoretical “spread” (sigma) assuming a Poisson distribution of data Poisson sigma == Sqrt(average signal) Average signal value calculation includes use of camera gain and read noise Camera values need to be correct

Poisson Data Reject Can be used on small stacks – even two or three images Very strong noise rejection May damage stars if the star sizes are not pretty similar across frames You need to try various sigma factors and look closely at the results

Workflow Calibration Registration Normalization Data rejection Combine (mean) DDP processing for luminance Sharpening for “preview”

Registration Tips Pick whatever image is well-framed as reference CCDIS is preferred – requires CCDInspector plug-in ($90) You can mix and match alignment methods as desired Help file has good explanations “Apply” alternatives Nearest neighbor – image must be well-sampled, alignment must be dead-on; be SURE you blink the stack Quadratic B-Spline is a safer choice

Normalization Pick your best quality image in the stack Normalization is CRITICAL to data rejection It can be repeated multiple times if needed

Data Rejection Tips Use your best-quality image Try for 1-2% data rejection on this reference image by adjusting the “factor” and looking at the “Info” window STD Sigma Reject is a good all-purpose choice Poisson Reject is well-suited for small stacks or nasty problems

Color Combination Tips Create an LRGB composite Use the background adjustment option Boost the contrast for better visualization White adjustment options Choose a known-white object Select an entire spiral galaxy Very good starting point on color balance

CCDStack Help Version 2 includes much-improved help Context-sensitive help (F1) Expanded information on various processing options CCDWare support forums are monitored and used by program authors

References Streaming video tutorial: http://www.ccdware.com/tutorials/ccdstack/overview.html Version 1 tutorial: http://www.ccdware.com/Files/CCDStack%20Tutorial.pdf Original SIG presentation: http://www.oc-aisig.org/docs/tutorials/ Where to get CCDStack: http://www.ccdware.com/