Void Lensing Cross-Correlation Meter v1: Synthetic Mocks Calibration and Mocks-Grid Tuning

This RESULTS note documents the Tier 2 (instrument) calibration of the Void LensingCross-Correlation Meter v1 on controlled synthetic mocks, together with a subsequent mocksgrid tuning run that maps the meter’s behavior across a broader synthetic domain. Theinstrument estimates three primary observables from stacked void–lensing convergence profiles:wall-slope coefficient of determination R2wall, shoulder-detection AUROC AUROCsh, and aninterface-count scaling exponent β. The preregistered calibration gates require R2wall ≥ 0.98(H1), AUROCsh ≥ 0.90 (H2), and ensemble interface-count bias βbias ≡ ⟨|βest −βtrue|⟩ ≤ 0.10(H3). Using the PyTwinPeaks mocks specification void_lensing_meter-mocks-v1.jsonand grid experiments driven by T2_void_lensing_meter_synthetic_mocks_v1.py, we findthat all three gates are simultaneously satisfied: the canonical calibration run achieves R2wall ≈ 1.0, AUROCsh = 1.0, and βbias = 0.0, and a tuned mocks-grid run retains R2wall = 0.9999999999999553, AUROCsh = 1.0, and βbias = 0.0. All claims are tied to specific artifacts (PNG, CSV, JSON) and commits, making this package suitable for arXiv/Zenodo submissionand for reuse as a T2-certified void-lensing meter in downstream tiers.