Resolving M3 to the core with lucky imaging

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timh
Posts: 218
Joined: Mon Aug 26, 2019 5:50 pm

Re: Resolving M3 to the core with lucky imaging

Post by timh »

Ok Brian,

I will accept the challenge - it is a fair test and actually should in theory be easily achievable with the ASI 294MM set at the 2.32 micron option to bring the image scale down to 0.475 arcsec/pixel?

Tim
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oopfan
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Re: Resolving M3 to the core with lucky imaging

Post by oopfan »

Tim,

Here is another double star in Hercules at higher declination and separated by 2.6 arcsec. The only thing to be mindful of is that the primary star is 67% more luminous than the secondary. I can't find a star orbit for it.

Star data courtesy of Sky & Telescope
Star map courtesy of Computer Assisted Astronomy (C2A)
Double Star SAO 47220 (Her).jpg
Double Star SAO 47220 (Her).jpg (101.05 KiB) Viewed 342 times
By the way, I didn't know until now that the ASI294 has a switchable pixel size! So that is great that you can go down to 2.3 microns.

Brian
timh
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Joined: Mon Aug 26, 2019 5:50 pm

Re: Resolving M3 to the core with lucky imaging

Post by timh »

Yes indeed. It is a bonus feature that I didn't even know I was getting when I bought it. The lucky imaging on M3 above only worked at the small pixel scale which sort of makes sense.
Tim
timh
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Joined: Mon Aug 26, 2019 5:50 pm

Re: Resolving M3 to the core with lucky imaging

Post by timh »

A big improvement!

Just on the off chance that anyone is still following or reading down this far on this thread about using lucky imaging for deepsky and is interested in trying it. :-)

The method described above relied entirely upon the PIPP quality algorithm which is based upon maximising pixel to pixel contrast to select the best quality frames. PIPP is OK for initial selection but it seems to make a very big difference to the final result if you then visually select using a tool like BLINK in PI.

Below I took the same MONO camera SER file of M3 as above -- initially selected 300/ 5700 using the quality algorithm in PIPP and then further weeded these down to the best 108 x 130ms frames by visual inspection using BLINK in PI. These best frames were then stacked using staralign and integrated in PI. The final image below at an image scale of 0.475 arcsec/ pixel is really a lot better than those above with most star cores taking up just one pixel. This time there clearly are star pairs resolved by only 3-4 pixels corresponding to a resolution of better than 1.5 arc sec.

Clearly it is crucial to be quite careful about only including the good seeing frames in the final integration! Below is the final 14s image and blow up of the core within which the separation in pixels can be counted and converted to arcsec.
Attachments
M3 core resolution at 0.475arcsec pixel-1.JPG
M3 core resolution at 0.475arcsec pixel-1.JPG (113.54 KiB) Viewed 330 times
M3coreBlinkselect.JPG
M3coreBlinkselect.JPG (43.48 KiB) Viewed 330 times
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oopfan
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Re: Resolving M3 to the core with lucky imaging

Post by oopfan »

Hi Tim,

Wow! Huge difference. I, too, handpick frames for AP. I'm old school. I trust technology but only to a certain extent. That's why I ramble on about my "rules of thumb". Too many young'uns are too trusting.

By the way, are you slightly out of collimation?

What would be interesting is this: A graph of rejection ratio versus exposure. On the same night, capture video for a range of sub-second exposures, and then use your technique of weeding out the bad from the good, then calculate the ratio of how many rejected versus the number of frames captured.

Brian
timh
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Joined: Mon Aug 26, 2019 5:50 pm

Re: Resolving M3 to the core with lucky imaging

Post by timh »

At the risk of turning this into War and Peace --- another useful improvement

It is perhaps not very practical to have to select the best 'lucky' frames visually - something I found that worked and simulated visual inspection pretty closely is to select subframes electronically based on 1) number of stars 2) SNR (noise) and 3) Eccentricity

Using subframeselector in PixInsight I used Eccentricity < 0.73 AND Stars > 70 AND SNRWeight >1300 as selection criteria (obviously appropriate actual values will vary from case to case) to run a selection which yielded about 5% of 'good frames. which when aligned and integrated produced about almost as good a resolution as when manually selected (just a question of refining the values)but with the advantages of a) being much easier and quicker to do and b) integrating more frames and so producing a deeper final picture
Attachments
M3core_284x130ms_subframe selected_Eccentricity_SNRW_Stars.JPG
M3core_284x130ms_subframe selected_Eccentricity_SNRW_Stars.JPG (76.07 KiB) Viewed 315 times
timh
Posts: 218
Joined: Mon Aug 26, 2019 5:50 pm

Re: Resolving M3 to the core with lucky imaging

Post by timh »

And now finally - last post from me on this topic - just to try and summarise how to generate a high resolution (about 1.4 arcsec FWHM according to PixInsight) image of the core of M3.

Also a final high resolution image that is now in colour! (obtained by combining the MONO and RGB data)

The image below were created from the same captures and files as described above except now processed in a better way. Since I struggled to work out how to do it all I thought that some details of the overall process maybe useful to others. To summarise these were the steps..

1) 320 x 240 pixel SC capture of a 16 bit RGB SER file comprising ~2600 x 100ms frames at gain 570 using an ASI 294MC camera at image scale 1.05 arcsec/ pixel.

2) 640 x 480 pixel SC capture of a 16 bit MONO SER file comprising ~5700 x 130ms frames at gain 570 using an ASI 294MM camera at image scale 0.475 arcsec/ pixel

3) The file 1) was debayered and , along with 2) processed using PIPP into thousands of individual FIT files

4) The .FIT files were then analysed in PixInsight subframe selector and the best quality~ 6% based on Eccentricity, SNRweight and thenumber of Stars selected and saved.

5) The two thus selected sets MONO and RGB files were each then each aligned and registered versus the same individual best MONO frame (in order that the two eventual integrations should be aligned to eachother and converted to an equivalent angle/ pixel scale)

6) The two sets of registered files were then integrated and identically cropped. The RGB file was colour corrected -using colour calibration and SCNR and then both the MONO and RGB integrations were stretched to non-linear using Histogram transformation

7) The RGB integration was then recalibrated with the high resolution luminance data from the MONO integration. To achieve this the luminance channel was extracted from the RGB file and then recalibrated to the luminance of the MONO integration (using the Linear fit tool). The recalibrated RGB luminance file was then added to the MONO integration using the Pixelmath MAX () function and the resultant modified luminance file then merged back into the RGB file.

8) Final slight adjustment with curves and MLTnoise removal.

Below are the finally obtained images - a high resolution colour image of the core of M3 as was the original aim. PI estimates the resolution of the image at about FWHM 1.4 arcsec. It comprises 157 x 100ms of RGB files and 149 x 130 ms of higher resolution MONO files and so represents a total integration time of 35s (as compared to a total imaging time of about 15min -- so a small amount of imaging but a lot of processing)

TimH
Attachments
M3_Newt_0.9X_300421_157 x100ms files_gain570_subframe_selected_regversusmonointegration1_010521frame1_pixintegrate_COL_SCNR_trans_curves_MLT.jpg
M3_Newt_0.9X_300421_157 x100ms files_gain570_subframe_selected_regversusmonointegration1_010521frame1_pixintegrate_COL_SCNR_trans_curves_MLT.jpg (54.63 KiB) Viewed 310 times
M3core_FWHM~1.4_incolour.JPG
M3core_FWHM~1.4_incolour.JPG (69.3 KiB) Viewed 310 times
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