Verify: logodds 333.123, 99 matches, 0 conflicts, 115 distractors after 213 field objects.ġ08 matches, 424 distractors, 2 conflicts (all sources) These can be seen as distractors and conflicts? in the debug output of the program: verify: logodds -1.38629, 0 matches, 0 conflicts, 1 distractors after 0 field objects. Query images may contain some extra stars that are not in your index catalogue, and some catalogue stars may be missing from the image. With that it can try to match all other stars with its know star catalog. The resulting triangle will give the program the hint on how all other stars must be translated to match the given triangle.
Astrometry website solve verification#
After a similar triangle is found (this will happen a lot), the process goes into the verification step. In reality we have to make the lookup multiple times to also search for flipped and/or inverted variations. IMO it simply boils down to the fact that the shape of a triangle is defined by two parameters, e.g. It should be obvious that this eliminates any rotation and scaling in the question asked, so any similarly shaped triangle will match, regardless of orientation or size. We basically search for the normalized blue vectors (or ones that are pretty close). In the picture below I tried to visualize how we can search for similar triangles by reducing the question to two numbers. Basically a KD-Tree can optimize the question "give me the closest point(s) to X/Y". The one used by is optimized for the pre-built index files, so they can be accessed very fast and without much (memory) overhead. For this problem gets technically more complicated, as we need to search for two "hashes". In this simple one-dimensional case we could e.g. be the angle between two lines.įrom here it should be "obvious" how one can use this approach to drive a search. The value should vary very little if the overall properties also vary little, so from looking at the delta of both translation results we can deduct how similar the two objects were. a double value).Ībstractly speaking we want to map numerous properties to a single value. I would rather say that they perform a geometric transformation where the result must be a one-dimensional value (e.g. checksum or hash-table bucket distributions). Real hashes should normally give very different results for small changes in the input (e.g. IMO the word "hash" is a bit misleading here as it has not much in common with real hashes. The papers talks about geometrically hashed lookup. So this is where the real magic happens IMO. So we have a triangle and want to know if a similar triangle exists anywhere in the selected index.
Astrometry website solve code#
Maybe they should have better named them "Asterism" (the code has a lot of very bad naming, as we soon will discover, took me hours to figure out).
But by my own experiments they seem to be just triangles. The cited paper always talks about 4 stars that form a quad. Now the algorithm goes into creating quads. In the end you need a list of coordinates for stars (plus optional flux/intensity/brightness and background). I will not go into details on how does it, just note that you can either use its internal simplexy algorithm or use SExtractor. As others already pointed out, the main input to the whole process is a list of stars. I've been trying to figure out the technical details of for quite some time.
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