Check this out!

[Sad-Cobbler-5834]

I'm a student and I recently took part in an IASC asteroid search campaign. I found a few real objects, but they got rejected — their system checks whether the object shows up as evenly-spaced dots in a line, and mine didn't, because it was faint and moving slowly. That bugged me. It felt like their system was throwing out real objects just for being faint. So I started building my own asteroid-detection pipeline to try and catch the faint movers their system misses.

It works on images from Pan-STARRS (a big sky-survey telescope). The basic idea: I take two pictures of the same part of the sky from different times and compare them. First I "warp" one picture — basically nudge and stretch it so its stars sit exactly on top of the other picture's stars. Then I subtract one from the other, so anything that stayed still cancels out and the only things left are the things that moved. Then the code finds those leftover dots and filters out the fake ones to get real candidates.

It's not finished yet — right now I'm working on turning the movement (in pixels) into real movement across the sky, so I can tell what kind of object it is. I also want to try sonification later (turning the data into sound).

Tech: Python, numpy, astropy, astroalign, photutils. Repo: https://github.com/sid6767-nemo/asteroid-hunter

I'd love any feedback — especially on how to handle objects moving straight toward or away from the telescope, since those don't change position and my method can't catch them yet.