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"I break for .. dark skies."

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#1 Ballyhoo

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Posted 03 June 2020 - 12:19 AM

90 sec M101 sub, Channel L,  bortle 5 versus 4.

 

Wow, what a difference when I leave the comfort of home and drive 35 miles east, from suburban San Diego to rural San Diego County.

Also eleveation is about 3500' for the dark sky site.

 

The fits header info is idential with the exception of the gps coordinates. 90 seconds,  imaged at  FR of 5.25 with my 5" refractor.

 

For me, a really dark site is where you can without strain see all four stars in the little dipper, which of course I could in the rural site. I have a half rural site I drive to, about half the distance; and for that you have to strain some, or at least, the fainter two stars in the little dipper are not so obvious. 

 

I want to get one of those old 80s license plate holder "I break for .. dark skies."  Because I really do. For me, it really is worth all the extra work of going to a dark sky site. 

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  • dark skys.jpg

Edited by Ballyhoo, 03 June 2020 - 12:24 AM.

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#2 Sam Danigelis

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Posted 03 June 2020 - 02:20 AM

If you happen to have a plate frame custom made, be sure to say: "I Brake..." (not break) 😀
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#3 TOMDEY

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Posted 03 June 2020 - 02:49 AM

If you happen to have a plate frame custom made, be sure to say: "I Brake..." (not break)

Actually, ~break~ might be more apropos, otherwise the ironic contradiction regarding the brake lights.    Tom



#4 Sam Danigelis

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Posted 03 June 2020 - 04:34 AM

Actually, true, good point! Ballyhoo, agreed, I do the same. My backyard is in town, Bortle 5. I drive 35 miles out to my dark site, Bortle 3/4, and a big difference!

#5 mtc

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Posted 03 June 2020 - 07:29 AM

Dark skies are awesome and memorable. I live in a Bortle 6 zone and had the opportunity to go to a Bortle 3. I had 10minutes of dark skies before the clouds rolled in..What a difference! 15sec on an unmodded Canon T5i @ iso3200.

Bortle3-6-15sec3200iso-2.jpg


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#6 Peregrinatum

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Posted 03 June 2020 - 10:31 AM

Nah, I think Bally is saying he break dances for dark skies


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#7 Ballyhoo

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Posted 03 June 2020 - 11:23 AM

If you happen to have a plate frame custom made, be sure to say: "I Brake..." (not break)

LOL  I did not even realize they are two different words but I thought the expression, or at least when I first saw " i brake for" comes from brake dancing...  

 

or is it break dancing? 

 

edit

 

Wade would know. He knows everything. 


Edited by Ballyhoo, 03 June 2020 - 11:25 AM.


#8 calypsob

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Posted 04 June 2020 - 08:09 PM

Darkskies are decompression therapy for me. I recommend 2 doses a month
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#9 ChrisWhite

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Posted 05 June 2020 - 06:35 AM

Darkskies are decompression therapy for me. I recommend 2 doses a month


I have a hard time traveling to inage, so I took the obsy treatment instead. (More of a time release treatment) Granted, my skies are not bad to begin with, but there is a major difference between my house (5mi from burlington) and property I have 45 min away.

Edited by ChrisWhite, 05 June 2020 - 06:36 AM.


#10 WadeH237

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Posted 05 June 2020 - 08:31 AM

Wade would know. He knows everything. 

Hardly.

 

My brain is filled with a bunch of useless trivia, fragments of potentially useful stuff - but mostly lyrics to 80's music.  What I do know is how to find answers, either by experimentation or by looking things up.  For example, regarding "brake dancing" vs "break dancing", it is none of the above.  It is actually "breakdancing".  Although, "brake dancing" does make me visualize someone partying with disassembled car parts.

 

Regarding dark skies, it's good that you have access to these skies just 35 miles away.  I have to travel much further to get significant improvement over my home sky.  Also, I think that there is more data in the image on the left than you realize.  With proper calibration and an appropriate stretch, you might be surprised at what's possible.  That said, dark skies make everything much easier.


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#11 Jon Rista

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Posted 05 June 2020 - 12:27 PM

Also, I think that there is more data in the image on the left than you realize.  With proper calibration and an appropriate stretch, you might be surprised at what's possible.  That said, dark skies make everything much easier.

It isn't that the image on the left DOES NOT have the same object signal as the image on the right. It most definitely does, given the exposure length and system were the same. 

 

The difference is how much EXTRA JUNK you ALSO have in the image on the left. It is all that extra junk that makes light polluted imaging such a monstrous PITA. :p For a given system and exposure, the object signal is indeed going to be the same from any location. What changes is how much unwanted, unhelpful, SNR-decimating junk signal you get AS WELL, and it is that unwanted junk that decimates SNR...or worse.

 

I've actually run that experiment though...to see "what I had" in my light polluted subs compared to dark site subs. There really is just no comparison...light polluted data just can't hold a stick to good, clean dark site data:

 

Identical exposures:

nz8iRnv.jpg

 

After normalizing:

CnZCEz3.jpg

 

It would take dozens of the frames on the left, to equal just one of the frames on the right. Same object signal...it is just that all that extra signal from LP decimates the SNR of the frame on the left. So it takes significantly more signal in total to average out all the extra noise. Thing is, it isn't just extra noise...its extra signal, and those signals usually leave behind something more than just random per-pixel noise. So even if you in a purely mathematical sense normalize SNR...you still usually don't get the same IQ after stacking frames on the left...



#12 WadeH237

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Posted 05 June 2020 - 01:19 PM

It isn't that the image on the left DOES NOT have the same object signal as the image on the right. It most definitely does, given the exposure length and system were the same. 

 

The difference is how much EXTRA JUNK you ALSO have in the image on the left. It is all that extra junk that makes light polluted imaging such a monstrous PITA. tongue2.gif For a given system and exposure, the object signal is indeed going to be the same from any location. What changes is how much unwanted, unhelpful, SNR-decimating junk signal you get AS WELL, and it is that unwanted junk that decimates SNR...or worse.

All true, but I think that your "after normalization" example is a much better comparison of the true effect of light pollution than comparing images that are not normalized (which is what was in post 1).



#13 Jon Rista

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Posted 05 June 2020 - 03:36 PM

All true, but I think that your "after normalization" example is a much better comparison of the true effect of light pollution than comparing images that are not normalized (which is what was in post 1).

It may look better, but in the end, the left hand side is the same either way (non-normalized or normalized). The true effect of light pollution is an increase (often significant) in noise, and additional unwanted signal (that frequently cannot be removed...note the junk signal around alcyone; this junk cannot really be modeled and removed, certainly not easily, and even with extensive care, the simple fact of the matter is these signals are blended and largely inseparable, so removal of some of one, inevitably results in removal of some of the other).

 

All I did to normalize, was balance out the background sky offsets. That is the sole difference between the top row of frames and the bottom. So the left (polluted) frame had some offset subtracted from it, in order for it to have the same offset, and thus allow exactly the same stretch and have the data look the same, as the right (dark sky) frame. (Actually, all of the examples use exactly the same stretch...the top left example simply shows how much brighter the overall signal is with the LP signal, whereas the bottom left just shows the object signal on top of a normalized offset with the right frame.) There was otherwise no other processing done, so the bottom is just viewing the same data with a darker black point than the top. 



#14 WadeH237

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Posted 05 June 2020 - 04:51 PM

It may look better, but in the end, the left hand side is the same either way (non-normalized or normalized). The true effect of light pollution is an increase (often significant) in noise, and additional unwanted signal (that frequently cannot be removed...note the junk signal around alcyone; this junk cannot really be modeled and removed, certainly not easily, and even with extensive care, the simple fact of the matter is these signals are blended and largely inseparable, so removal of some of one, inevitably results in removal of some of the other).

 

All I did to normalize, was balance out the background sky offsets. That is the sole difference between the top row of frames and the bottom. So the left (polluted) frame had some offset subtracted from it, in order for it to have the same offset, and thus allow exactly the same stretch and have the data look the same, as the right (dark sky) frame. (Actually, all of the examples use exactly the same stretch...the top left example simply shows how much brighter the overall signal is with the LP signal, whereas the bottom left just shows the object signal on top of a normalized offset with the right frame.) There was otherwise no other processing done, so the bottom is just viewing the same data with a darker black point than the top. 

You and I know all of this, but I'm not sure that most of those in this (beginner and intermediate) forum do.

 

The reason for my comment is that I see lots of examples of people posting screen shots that are intended to do some kind of comparison, and often the A and B images are not processed to normalize them as you've done here.  For example, the first post shows an exposure from a light polluted area that is not stretched properly.  By "properly", I mean that that the brightness region where faint (and even medium bright) details exist are not stretched to show what is there.  In your comparison showing normalized data, the true nature of the problem is better demonstrated.  If all you did was to show the "Identical exposures" images in your post, most of the nebulosity is hidden, even though it's there (albeit quite noisy).


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#15 Jon Rista

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Posted 05 June 2020 - 04:54 PM

You and I know all of this, but I'm not sure that most of those in this (beginner and intermediate) forum do.

 

The reason for my comment is that I see lots of examples of people posting screen shots that are intended to do some kind of comparison, and often the A and B images are not processed to normalize them as you've done here.  For example, the first post shows an exposure from a light polluted area that is not stretched properly.  By "properly", I mean that that the brightness region where faint (and even medium bright) details exist are not stretched to show what is there.  In your comparison showing normalized data, the true nature of the problem is better demonstrated.  If all you did was to show the "Identical exposures" images in your post, most of the nebulosity is hidden, even though it's there (albeit quite noisy).

Gocha. Yeah, I do try to be clear about what I'm showing, and I also do agree that proper normalization to get a proper comparison is important. I can understand beginners not normalizing though, as there are some specific techniques involved in properly normalizing data like that. I wonder if it would be possible to create a little script in PI that could find "true background sky" pixels in both a reference and target frame, compute the mean for both, then figure out what to add or subtract to one or the other to normalize the two with a simple offset shift. That might make it easier for beginners to share properly normalized comparisons...


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#16 Ballyhoo

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Posted 05 June 2020 - 08:15 PM

Hardly.

 

My brain is filled with a bunch of useless trivia, fragments of potentially useful stuff - but mostly lyrics to 80's music.  What I do know is how to find answers, either by experimentation or by looking things up.  For example, regarding "brake dancing" vs "break dancing", it is none of the above.  It is actually "breakdancing".  Although, "brake dancing" does make me visualize someone partying with disassembled car parts.

 

Regarding dark skies, it's good that you have access to these skies just 35 miles away.  I have to travel much further to get significant improvement over my home sky.  Also, I think that there is more data in the image on the left than you realize.  With proper calibration and an appropriate stretch, you might be surprised at what's possible.  That said, dark skies make everything much easier.

no doubt about what one can get from light polluted skies.    I was asking myself this, and maybe you could retort: for example with the OP left image, the same data is there for the target object in both images, right?  But the difference is the light pollution is greatly decreasing the contrast. So, if gradient removal is really good, wouldn't that just be able to suck out the light pollution? Or does that not work because in doing that, it also takes away some target data?  But for me just looking at the two, on the face it is rather astounding how much of an improvement dark sky yields.  It almost makes me want to move out in the boondocks -- almost.  I would have to think that dark skys are just worth all the trouble of packing things up and driving -- unless gradient removal techinques can get that much better.

 

 

edit, some of my above questions were addressed by John Rista as I did not immediately read all the posts before my above response. in the end it seems to always boil down to SnR. 


Edited by Ballyhoo, 05 June 2020 - 08:19 PM.


#17 WadeH237

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Posted 06 June 2020 - 09:53 AM

So, if gradient removal is really good, wouldn't that just be able to suck out the light pollution?

Light pollution is not just a gradient.  It's also a pedestal.  And significantly light pollution is mostly pedestal.

 

I'm having a hard time thinking of a concise, understandable way to say the following, but I'll give it a try...

 

Let's say that you have an object that has some faint areas that are only 7 or 8 ADU worth of signal.  Now let's assuming that you have 200 ADU worth of light pollution.  If you do nothing to identify and subtract the light pollution, then you'll be looking at an area in the image where the are pixels that are 200 ADU in proximity with pixels that are 207 ADU.  Those pixels are going to be quite hard to distinguish from each other by eye.  Now if you could subtract, say 195, ADU from every pixel in the image, then you'd be looking at an area with background sky at 5 ADU and your object's faint data at 12 ADU,  Those pixels are easy to distinguish by eye.

 

Now that is a very simplistic view of the situation.  Assume that you have a "perfect" camera with zero read noise.  The shot noise contribution from a 200 ADU light polluted sky will be 200, plus or minus 14 ADU (which is close to the square root of 200).  So you get a range of sky values that are varying mostly between 186 and 214 ADU.  Your 7 ADU faint object signal will be plus or minus 2.5 ADU (square root of 7).

 

Given the uncertainty from the shot noise, you can (and will) have some faint object pixels that are lower values than some background sky pixels.  Obviously, this would make it very hard to find that faint signal.  You can see this pretty well in Jon's normalized example.  The light polluted image is grainy because some of the pixels are simply indistinguishable from the background sky because their pixel value is lower than the sky values due to the shot noise.

 

There are two ways to attack this problem.  The best way is to reduce the signal from the background sky, which is what you get by imaging from a darker sky.  You can also reduce the uncertainty in the background sky signal by greatly increasing the total integration time, which helps to reduce the uncertainty of the pixels (since signal grows faster than noise over time).  Note that there is a point where this simply becomes impractical.

 

My point in all of this, is that to make a fair comparison by eye, you need to go through the exercise of trying to subtract the background sky values (and also addressing gradients, which are likely also present).  The STF auto stretch is not going to do this for you, so it's a manual thing.  Also, none of own perfect cameras, so there is also read noise, which impacts this a bit.

 

I'm thinking about Jon's idea of writing a script to automate this.  I'm really, really new at writing PI scripts (I have one nearly finished that will help to fit narrow band images to each other based on selected background and highlight areas, but it's my first one).  When I get some time (which oddly enough is harder to do right now, since I'm working full time from home), I may play with the idea...


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#18 fmeschia

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Posted 06 June 2020 - 02:09 PM

So, if gradient removal is really good, wouldn't that just be able to suck out the light pollution? 

I prepared a video with a simulation to demonstrate why light pollution affects signal-to-noise ratio beyond gradient removal recovery: https://www.youtube....h?v=WGjq6eGtkcU


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#19 Ballyhoo

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Posted 06 June 2020 - 02:41 PM

Light pollution is not just a gradient.  It's also a pedestal.  And significantly light pollution is mostly pedestal.

 

 

I'm thinking about Jon's idea of writing a script to automate this.  I'm really, really new at writing PI scripts (I have one nearly finished that will help to fit narrow band images to each other based on selected background and highlight areas, but it's my first one).  When I get some time (which oddly enough is harder to do right now, since I'm working full time from home), I may play with the idea...

thank you so much for the thoughtful response.  


Edited by Ballyhoo, 06 June 2020 - 02:41 PM.



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