Understanding SNR Weight graph in Pixinsight

[Dialtoan]

I have recently started using Pixinsight, I'm following the workflow outlined on the Light Vortex website.

I previously posted this over at Cloudy Nights but received no responses other than the suggestion to post it over to this group.

After running SubFrameSelector, I first excluded a bunch of subs with the worst Eccentricity and FWHM values. When I then looked at the SNR Weights, I noticed that the excluded subs seemed to be in a somewhat regular pattern. (I realize the overall trend of the data is down, but I believe that is because the sky was getting darker as I was taking these subs)

I am not autoguiding, these were 90s exposures with a DSLR and a 72mm refractor mounted on a Skywatcher EQ5 Pro.

Is this what periodic error looks like? Or perhaps I have not done a good job of making my mount east heavy? Any suggestions as to the cause of this? Would guiding help?

Dan

[pfile]

if you were measuring eccentricity then that would say something about how good your guiding/tracking is. but this is an SNR (signal-to-noise ratio) weight plot.

still, since more than likely you'll have several worm periods in any given frame, i don't think the eccentricity measurement would say anything about your PE when measured from frame to frame. the periodic error would probably just manifest as RA elongation (eccentricity in the RA direction).

anyway: this is what mike schuster said about the SNRWeight measurement in the original SFS script documentation

The signal to noise ratio weight estimate for the subframe. SNRWeight equals MeanDeviation2 / Noise2. SNRWeight is unnormalized approximation to the current NoiseEvaluation weight used by the ImageIntegration process. In a subframe integration, the ratio between a subframe's SNRWeight and the reference subframe's SNRWeight approximately equals the NoiseEvaluation weight of the subframe.

The significance of the unnormalized SNRWeight and the normalized NoiseEvaluation weight is that a weighted subframe integration using these weights is an approximate maximum likelyhood estimator for pixel values that correspond to background limited targets, without requiring additional information such as exposure times or sensor parameters. See the ImageIntegration documentation for more information.

Note that SNRWeight and NoiseEvaluation weight are relative and not absolute measures of signal to noise ratio. Their formulation assumes that the subframes represent observations of the same target and that the subframes have similar background gradients

does the Median parameter correlate with this graph? maybe the steady decline indicates worse and worse gradients in the images. not sure.

rob