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Calorimeter Energy Response Inversion L1-483

Particle PhysicsDetector calibrationδ=5 · challengingL_DAG = 3.5📋 Stub — not mineable
📋

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

Calorimeter Energy Response Inversion: Calorimeter calibration: determine energy response and resolution functions from test beam or in-situ data. The forward operator produces the measurement through a 3-node primitive DAG (M.shower.em_hadronic…); recovery is posed as a parameter_estimation problem. Difficulty tier delta=5 with effective condition number kappa_eff~500; aging_degradation_percent, temperature_variation_effect set the accuracy floor at the Omega boundary. See the forward_model field for the closed-form equation.

L-DAG

G.structured -> S.clustering.topo_clustering -> O.chi2.energy_response
G.structuredS.clustering.topo_clusteringO.chi2.energy_response

Well-posedness W

Existence:
true
Uniqueness:
true
Stability:
conditional
κ:
10000

Existence of the recovered energy_calibration_coefficients is guaranteed within the declared Omega bounds. Uniqueness holds on the measurement-supported subspace; out-of-support modes are controlled by declared priors. Stability is conditionally stable (kappa_eff ~= 500); aging_degradation_percent dominates the stability cliff; the remaining mismatch parameters contribute higher-order bias terms. Detector gaussian sets the irreducible data-fidelity floor.

Solvability C

Solver class:
classical [in_situ_intercalibration or test_beam_chi2_minimization]
Convergence rate q:
2
Complexity:
O(N_cells * N_events) for calibration coefficient determination per iteration

Specs (0)

No L2 specs registered yet for this principle.