Hidden
Stellar Coronagraphy — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| coronagraph_mask_centering | -0.014 – 0.046 | lambda/D |
| wavefront_error_(wfe) | -14.0 – 46.0 | - |
| stellar_leakage | -0.139998 – 0.459998 | contrast |
| speckle_lifetime | -14.0 – 46.0 | s |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionCoron + gradient | 0.722 | 29.96 | 0.916 | 0.78 | ✓ Certified | Lim et al., ApJ 2024 |
| 2 | CoronFormer + gradient | 0.646 | 25.96 | 0.831 | 0.77 | ✓ Certified | Gebhard et al., A&A 2022 |
| 3 | SpeckleLearn + gradient | 0.600 | 23.3 | 0.743 | 0.85 | ✓ Certified | Yip et al., AJ 2020 |
| 4 | KLIP + gradient | 0.596 | 23.12 | 0.736 | 0.85 | ✓ Certified | Soummer et al., ApJ 2012 |
| 5 | PCA-ADI + gradient | 0.575 | 22.69 | 0.719 | 0.8 | ✓ Certified | Amara & Quanz, MNRAS 2012 |
| 6 | CNN-Coronagraph + gradient | 0.565 | 21.97 | 0.689 | 0.85 | ✓ Certified | Gonzalez et al., AJ 2018 |
| 7 | LOCI + gradient | 0.542 | 20.92 | 0.642 | 0.88 | ✓ Certified | Lafrenière et al., ApJ 2007 |
| 8 | ANDROMEDA + gradient | 0.479 | 18.97 | 0.548 | 0.85 | ✓ Certified | Cantalloube et al., A&A 2015 |
| 9 | ADI + gradient | 0.468 | 18.59 | 0.529 | 0.85 | ✓ Certified | Marois et al., ApJ 2006 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%