Claude AI on integration time limits for astrophotography

Interesting… I'd be curious to know how it came up with the 0.1-0.5% for the error from flat frames (I doubt those numbers are easily found on the Internet) and how the point of diminishing returns would change for a more light polluted location. In any case, this is a cool topic, so thanks for sharing!

How do you put a threshold number to ‘diminishing returns’ when it’s diminishing right from the start? And why is the improvement goal always double? Isn’t a 50% increase in SNR significant, or 25%, or 15%?..... I think integration time really depends on the target, what there is to get, and what you want to get. If you’re imaging a bright galaxy and there isn’t much in the way of tidal tails or IFN to pick up, or that you want to include, then there’ll be a point where additional SNR won’t make a noticeable perceptual difference. On the other hand, images like the recent M104 IOTD shows what can be done with integration time far beyond what most consider ‘diminishing returns’.

It also depends on your own available imaging time and number of targets you personally want to image/publish. Some like going deep, some prefer variety.

Claude’s response seems to be mixing integration time with exposure time and factors like seeing that have little to do with both.

Cheers,
Scott

Jerry,

Your question touches on a number of interesting things that I’ve given a bit of thought to in the past. First, I’ve experimented a bit with AI for solving some slightly mathematically challenging problems. A year ago I gave Claude and ChatGPT the same thermodynamics problem that I had worked out myself in some detail. At that time, Claude really struggled and after directing it through multiple revisions (around 6x) I finally gave up. It just couldn’t get there. On the other hand, ChatGPT did a much better job. I only had to give it 2-3 course corrections before it provided what appeared to be the correct solution. I recognize that within the last year, Claude has been much improved; however, the lesson is deeper than that. And here’s the lesson: If you don’t already have a pretty deep understanding of what the solution should look like—and why, you can get completely fooled by an answer provided by AI. To be clear, that not to say that AI isn’t a useful tool. It is but you need to be very cautious to probe the answer and you need to use other means to confirm that it makes sense.

In this case, Claude has pointed out something interesting. Years ago I first posted a calculation that expanded on the “Rule of Five” published in “The Handbook of Astronomical Image Processing 2nd Edition”, by Berry and Burnell (now out of print). My calculation (posted in other threads here on AB and on CN) showed how dark calibration (using stacked dark masters) affects noise in a stacked image, but that result might not be quite the same as how flats affect stack statistics. As Janesick points out in “Photon Transfer”, signal variation due to FPN varies directly with signal strength and can far exceed photon noise for some sensors—and that’s one of the three key reasons that we use flat calibration. But the issue that Claude has mentioned goes beyond simple image calibration. It extends to how the SNR is affected by FPN when you combine calibrated and dithered frames in the stack—and it’s not clear to me that the answer that you got from Claude is correct. For one thing, Claude doesn’t show its work and the details of why it is saying what it is saying are vague. Dithering is a key component to reducing the effects of FPN so it is a very important factor when you consider any limitation on SNR with respect to total exposure time.

I’ve got too many other things I’m working on at the moment to dig more deeply into this but perhaps when I get some time, I can give it a bit more thought. In the meantime, I would recommend approaching those results with some caution.

John