Kyah Adams, Embry-Riddle Aeronautical University; Tyler Barr, Embry-Riddle Aeronautical University; Di Wu, Embry- Riddle Aeronautical University
https://doi.org/10.64861/UTKE8310
Keywords: Space Situational Awareness, Space Domain Awareness, Orbital Debris, Space Debris Mitigation, Orbital Sustainability, MOCAT-MC, Monte Carlo Simulation, Debris Environment Modeling, Collision Risk, Fragmentation, Conjunction Assessment, Space Governance
Abstract:
The exponential growth of the orbital debris population is increasing collision probability, fragmentation events, and the risk of cascading instability in low Earth orbit. Effective management depends on space situational awareness (SSA), yet current mitigation and governance frameworks remain largely qualitative and disconnected from measurable environmental response. Catalyst addresses this gap through a simulation-driven governance evaluation framework integrating SSA-informed environmental characterization, historical two-line element data, a data-driven launch scenario, physics-based debris modeling and a Code World Model-trained large language model (LLM) for structured policy iteration._x000D_
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The physical modeling layer is implemented through the Embry-Riddle Orbital Capacity Assessment Tool (EMOCAT), an Embry-Riddle developed redesign that combines the object level Monte Carlo capabilities of the MIT Orbital Capacity Assessment Tool- Monte Carlo (MOCAT-MC) with the shell-based population accounting od the Source-Sink Evolutionary Model (SSEM). MOCAT-MC performs stochastic propagation of individual objects and resolves the mathematical evolution of orbital populations through launch injection, orbital decay, collisions, fragmentation, disposal, and maneuver related processes. SSEM aggregates these dynamics into altitude-shell source and sink terms that are better suited to policy-metric construction, threshold analysis, and comparison of governance interventions. EMOCAT therefore preserves object-level physical fidelity while producing shell-resolved environmental indicators that can be interpreted consistently across policy scenarios. _x000D_
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The launch scenario generator varies modeled launch years while preserving realistic spatial density, orbital-regime utilization, deployment behavior, and object characteristics. Multi-seed Monte Carlo propagation captures probabilistic variation in collisions, fragmentation, disposal outcomes, and compliance behavior across governance conditions over a ten-year horizon. Object level trajectories and stochastic event records generated through the Monte Carlo layer are translated through SSEM into policy-relevant measures of shell population, source and sink balance, debris persistence, collision exposure, and environmental stabilization. SSA-derived observables are mapped directly to simulation and governance metrics: spatial density informs debris growth and environmental stability; conjunction and encounter rates inform collision opportunity; fragmentation characteristics inform debris generation and cascade dynamics; and tracking accuracy and maneuver awareness inform collision-avoidance effectiveness and near-term risk reduction._x000D_
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Governance variables are translated into EMOCAT-compatible parameters, including post-mission disposal compliance, disposal reliability, fragmentation and passivation effectiveness, launch pacing, and maneuver-based collision avoidance. Catalyst translates policy conditions into scenario parameters and interprets modeled outcomes through four sustainability pathways. Physical sustainability measures population growth, altitude-shell density, collision opportunity, disposal success, and fragmentation burden. Economic sustainability evaluates avoided-loss proxies, maneuver burden, service-disruption exposure, and disposal or remediation investment. Conflict sustainability captures attribution ambiguity, critical-asset density, operator interaction, and escalation-relevant conditions. Governance sustainability evaluates traceability, compliance, verification strength, adaptability, and decision latency._x000D_
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Catalyst provides the evidence-bounded interpretation layer necessary for navigating the relationship between governance choices and EMOCAT system behavior. Rather than treating every model output as sufficient for a policy conclusion, Catalyst distinguishes supported physical findings, shell-level policy metrics, economic proxies, governance context, and claims that must be rejected because the required evidence is absent. The resulting architecture supports predictive simulation to governance analysis while preserving explicit evidentiary boundaries, reproducibility, and human review. _x000D_
Date of Conference: September 15-18, 2026
Track: Space Debris