Very Long Baseline Interferometry (VLBI) L1-106
Unclaimed Principle — open for contribution
This Principle is declared in the catalog but has no reference solver, no pinned dataset, and is not registered on-chain. There is no reward pool. Submitting a cert against this Principle today will record the cert for reproducibility but pay zero PWM.
To claim it as a Bounty #7 contribution: open a PR adding (1) a reference solver, (2) ≥1 dataset pinned to IPFS, (3) updates to the L3 manifest with dataset CIDs. After verifier-agent triple-review, the founders' 3-of-5 multisig signs PWMRegistry.register() and the Principle becomes mineable.
Forward model E
Very Long Baseline Interferometry (VLBI): radio interferometry produces the measurement through a 4-node primitive DAG L.radio_antenna_array -> L.cross_correlation -> L.fourier_synthesis -> int.spatial, with spatially-projected accumulation and additive Gaussian thermal/electronic noise. Recovery is posed as a linear inverse problem that inverts the forward operator to estimate the scene-side 2D brightness temperature. Difficulty tier delta=10 with effective condition number kappa_eff~30; calibration-level mismatch (ionospheric_delay, atomic_clock_drift, station_position_error) sets the accuracy floor at the Omega boundary. See the forward_model field for the closed-form imaging equation.
L-DAG
Well-posedness W
- Existence:
- true
- Uniqueness:
- true
- Stability:
- conditional
- κ:
- 600
Existence of the recovered 2D brightness temperature is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by the declared priors. Stability is moderately conditioned (kappa_eff ~= 30); ionospheric_delay dominates the stability cliff; atomic_clock_drift and the remaining mismatch parameters contribute higher-order bias terms. Additive gaussian thermal/electronic noise sets the irreducible data-fidelity floor, while TV / wavelet-sparsity / deep priors stabilise recovery at the ill-conditioned end of Omega.
Solvability C
- Solver class:
- iterative projection (ADMM / GAP) + optimisation [CLEAN, MS-CLEAN] | linear-operator + convex optimisation [Compressed-Sensing-VLBI, EHT-RML]
- Convergence rate q:
- 2
- Complexity:
- O(H * W * log(...)) per iteration