EXP-006 · research
Astro Denoise
Frontier knowledge hides behind the noise…
astro-denoise is a research proposal for a modular, reproducible benchmark of denoising methods on simulated Vera Rubin Observatory (LSST DC2) images. Any method — classical filter, trained network — plugs into the same protocol and runs on the same patches, and is scored not by how clean the image looks, but by what it does to the science: the completeness and purity of the faint-source catalog, compared against the DC2 truth catalog. BM3D and a U-Net are the first two references being explored — the platform is designed to grow as more methods are added.
A master's thesis in progress.
This started as a master's thesis and is still taking shape. The first working version is in place: four open modules (metrics engine, BM3D and U-Net baselines, and an orchestrator with a terminal dashboard), a curated multi-band block of sky regions prepared for distribution on Hugging Face, a one-command scaffold so any researcher can plug in a new method, and initial BM3D-vs-U-Net comparisons with a proper train/eval split. The experiments are running — in the open, right here — but there is more work ahead before this becomes a finished benchmark.
- Q1Can a denoiser raise completeness without hurting purity — or does it invent sources that were never there?
- Q2Which family of methods — classical filters or learned models — is more likely to help at LSST DC2 depth, and by how much?
- Q3Can the whole benchmark run reproducibly end to end — versioned data, methods, metrics and bibliography?
Research
Research artifacts that cut across the lab’s experiments — papers, datasets, benchmarks, models. The first entries come from this track; the list grows only as work is actually published.
- in preparation
Papers
Publications and preprints produced by lab experiments.
- in preparation
Datasets
Open datasets, starting with astro-denoise benchmark data.
- in preparation
Benchmarks
Reproducible benchmark suites with documented assumptions.
- in preparation
Models
Trained models released alongside their training recipes.
FAQ
- What is astro-denoise?
- A research project benchmarking denoising methods on simulated Vera Rubin Observatory (LSST DC2) images. The goal is to recover faint galaxies without inventing ones that were never there — scored by science recovery (completeness + purity), not visual quality.
- What is denoising in astronomy?
- Astronomical images contain unavoidable noise from the detector and sky. Denoising aims to reduce this noise while preserving real sources. The risk: a denoiser can smooth out real faint galaxies or invent fake ones that were never there.
- What is LSST?
- The Legacy Survey of Space and Time — a 10-year astronomical survey conducted by the Vera Rubin Observatory in Chile. It will image the entire visible sky repeatedly, producing the deepest wide-field catalog of galaxies, stars, and transient events ever made.
- What is DC2?
- Data Challenge 2 — a simulated dataset that mimics what LSST will produce, created by the Dark Energy Science Collaboration (DESC). It includes realistic noise, PSF, and atmospheric effects, with a truth catalog telling you exactly which sources are real.