Dev
Stellar Coronagraphy — Dev Tier
(3 scenes)Blind evaluation tier — no ground truth available.
What you get
Measurements (y), ideal forward operator (H), and spec ranges only.
How to use
Apply your pipeline from the Public tier. Use consistency as self-check.
What to submit
Reconstructed signals and corrected spec. Scored server-side.
Parameter Specifications
🔒
True spec hidden — estimate parameters from spec ranges below.
| Parameter | Spec Range | Unit |
|---|---|---|
| coronagraph_mask_centering | -0.024 – 0.036 | lambda/D |
| wavefront_error_(wfe) | -24.0 – 36.0 | - |
| stellar_leakage | -0.239998 – 0.36 | contrast |
| speckle_lifetime | -24.0 – 36.0 | s |
Dev Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | DiffusionCoron + gradient | 0.746 | 30.34 | 0.922 | 0.87 | ✓ Certified | Lim et al., ApJ 2024 |
| 2 | CoronFormer + gradient | 0.731 | 29.05 | 0.901 | 0.9 | ✓ Certified | Gebhard et al., A&A 2022 |
| 3 | SpeckleLearn + gradient | 0.679 | 26.89 | 0.855 | 0.84 | ✓ Certified | Yip et al., AJ 2020 |
| 4 | KLIP + gradient | 0.625 | 24.14 | 0.773 | 0.87 | ✓ Certified | Soummer et al., ApJ 2012 |
| 5 | CNN-Coronagraph + gradient | 0.616 | 23.8 | 0.761 | 0.87 | ✓ Certified | Gonzalez et al., AJ 2018 |
| 6 | LOCI + gradient | 0.591 | 23.01 | 0.731 | 0.84 | ✓ Certified | Lafrenière et al., ApJ 2007 |
| 7 | PCA-ADI + gradient | 0.574 | 22.82 | 0.724 | 0.78 | ✓ Certified | Amara & Quanz, MNRAS 2012 |
| 8 | ANDROMEDA + gradient | 0.543 | 21.54 | 0.67 | 0.8 | ✓ Certified | Cantalloube et al., A&A 2015 |
| 9 | ADI + gradient | 0.492 | 19.82 | 0.59 | 0.79 | ✓ Certified | Marois et al., ApJ 2006 |
Visible Data Fields
y
H_ideal
spec_ranges
Dataset
Format: HDF5
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%