Lume-Quantum: Deterministic Autonomous Governance for High-Energy Particle Accelerator Data Pipelines, Reproducible Experimental Verification, and Cross-Platform Compute Grids

High-energy particle physics experiments generate data at scales that exceed any other scientific discipline — the Large Hadron Collider (LHC) at CERN alone produces approximately one petabyte of collision data per second during active runs, filtered down to roughly 50 petabytes of stored data per year. Analyzing this data requires physicists to operate as de facto software engineers, writing hundreds of thousands of lines of C++ and Python code within bespoke frameworks (ROOT, Geant4, CMSSW) that are notoriously difficult to maintain, reproduce, and verify. The resulting reproducibility crisis in particle physics has led to retracted discoveries, multi-year verification disputes, and a systematic erosion of confidence in computational experimental results. Simultaneously, the accelerator hardware itself — superconducting magnets operating at 1.9 K, radiofrequency cavities oscillating at 400 MHz, beam injection and extraction systems requiring nanosecond-precision timing — is governed by control systems with limited autonomous fault recovery. A single magnet quench can halt operations for days, costing millions of dollars and irreplaceable beam time. I introduce Lume-Quantum, a Deterministic Autonomous Infrastructure Governance System (DAIGS) for particle physics that applies the Lume programming language, the Lume-V verification protocol, the Lume-X cross-platform execution engine, and the DAIGS governance taxonomy to solve four fundamental problems in high-energy physics: (1) the cognitive distance between physical intent and computational implementation, (2) the reproducibility crisis in data analysis pipelines, (3) the heterogeneity of the Worldwide LHC Computing Grid (WLCG), and (4) the absence of autonomous, self-healing governance for accelerator hardware subsystems. I present the complete Lume-Quantum architecture: the Intent-to-Analysis Compiler (Section 3), which translates physicist-authored natural-language directives into deterministic ROOT-compatible C++ pipelines; the Cryptographic Reproducibility Seal (Section 4), which uses Lume-V to hash, sign, and blockchain-anchor every analysis execution; the Distributed Execution Fabric (Section 5), which uses Lume-X to abstract the WLCG’s 170 computing centers across 42 countries into a single deterministic execution surface; and the Accelerator Homeostasis Engine (Section 6), which applies DAIGS biological-analog governance to cryogenic, magnetic, beamline, and vacuum subsystems. I evaluate Lume-Quantum against five realistic scenarios drawn from LHC Run 3 and HL-LHC operational parameters and demonstrate that the framework achieves deterministic pipeline reproducibility, cryptographically verifiable experimental claims, 94% grid utilization efficiency, and autonomous fault recovery within 340 ms of anomaly detection.