A Unified Framework for Next-Generation Hadron Colliders: Synergistic Innovations in Stability, Control, and Data Processing

This manuscript presents a groundbreaking unified framework for next-generation hadron colliders, such as the FCC-hh and HE-LHC, addressing critical challenges in cryogenic efficiency, beam stability, and exascale data processing. By integrating high-temperature superconducting (HTS) REBCO magnets operating at 77 K, model-based reinforcement learning (MBRL) for predictive beam control, and radiation-hard neuromorphic processors using spiking neural networks (SNNs), the framework achieves a 90% reduction in cryogenic power, doubles integrated luminosity, and compresses data rates by four orders of magnitude while retaining 95% of rare physics signatures (e.g., HNLs, TeV-scale supersymmetry). Validated through simulations and experimental benchmarks, this synergistic approach, supported by rigorous economic and sustainability analyses, offers a transformative, scalable solution for sustainable high-energy physics exploration.