From Noisy Photons to the Ring Nebula: M57 CCD Image Reduction

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What this project is

This project is an end-to-end CCD reduction pipeline that turns raw FITS frames into a scientifically calibrated RGB/LRGB image of M57. It includes the full calibration and “data cleaning for physics” stack—bias/dark/flat correction, registration, sky/background matching, channel scaling, and a controlled Lupton asinh stretch so the final image is both faithful and readable.

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Techincal difficulties I faced:

• Registration took way more patience than I expected. I’d nudge alignment and think it was fine, then I zoom in and see tiny red/blue fringes around stars making me say, “nope.” Chasing sub-pixel shifts was a lot of trial-and-error, and it was frustrating how one pixel could undo hours of work.
• Background/sky matching was surprisingly touchy. Different frames came in with slightly different sky levels and gradients, and it kept throwing off the whole color balance. I remember hesitating a lot here because I didn’t want to “clean” so hard that I erased faint real structure in the nebula.
• Color correction and LRGB blending felt like walking a narrow line. I wanted the colors to be scientifically precise, but every “smart” correction had a catch. When I wenttoo aggressive, the background noise would blow up, too gentle and the nebula looked flat and lifeless. I ended up adding guardrails (noise floors, caps, blending) mostly because I wanted something that looked believable and didn’t fall apart the moment I stretched it.

The process was a lesson in precision and humility. I quickly learned that real-world data is fragile; even a single pixel of misalignment creates false color artifacts that physics can't explain. While I am proud that the pipeline successfully recovered the distinct ionization shells of the nebula, comparing the result to Hubble is a stark reminder of the limits of ground-based imaging. The code did its job, but the atmospheric blurring and readout noise remind me that there is always room for better calibration frames and longer integration times. I want to thank Kirk Long for motivating me to take up on this project, even though he may not realize it.

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Project Details

Author: Sajal Gupta

Year: 2021

Tech: Python, NumPy/Scipy/AstroPy, Matplotlib

Features: End-to-end CCD calibration (Bias/Dark/Flat), sub-pixel image registration, robust sky background subtraction, and LRGB composition using a flux-preserving Lupton asinh stretch for true-color visualization.