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Estimating horizon from a photo on a clear night
From: Bill Lionheart
Date: 2024 Dec 14, 14:38 +0000

Suppose we have our approximate position only but it is a clear night at sea with lots of stars visible. We have a camera with good low light performance but the ship is of course pitching and rolling a bit, We take a photo of the horizon and this records stars up to a certain magnitude sensor pixel accuracy, we run astronomy.net's solve-field or similar on the photo so we now know the Ra/DEC of the camera pixel coordinates. We know the stars we see are above the horizon (assume no reflection), we also know there are stars that we cannot see below the horizon, as the star catalogue tells us where we are looking. So we can estimate the sea horizon and we can do it better the more stars we can see. Theoretically what is the relationship between the apparent magnitude of the stars we can see to the accuracy (say standard deviation) of our fit to the horizon? We could use multiple cameras on the same mounting to see up to the whole horizon. Roughly I am asking something like the number of stars within a given number of minutes of a typical great circle in the celestial sphere (below a certain apparent magnitude). Presumably such statistical properties of the distribution of stars has been studied to death? From an image processing point of view a "great circle Hough transform" would be the typical way to estimate the horizon, but we can also frame it probabilistically as a kind of regression problem. I suppose this will work better when you are closer to the galactic poles and there are more stars near the horizon (I am no great expert on astronomy as you can tell)

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