Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Tyler Barr, Junior
Lead Presenter's Name
Tyler Barr
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Di Wu
Abstract
Catalyst is an analyst grade mission console and offline simulation service that operationalizes the Embry Riddle MIT Orbital Capacity Assessment Toolbox Monte Carlo (E-MOCAT-MC) for Space Situational Awareness (SSA) aligned assessment, connecting scenario assumptions to time tagged events, quantitative stability indicators, and evidence preserving exports. E-MOCAT-MC serves as the authoritative generator of seeded scenario runs under explicit configuration control, ingesting locally served Two Line Element sets (TLEs) and persisting each run as a structured record containing configuration data, stochastic seeds, event logs, derived metrics, and replayable artifacts for deterministic analysis. Unlike systems that primarily visualize TLEs and object tracks, Catalyst is designed to compute reproducible scenario outcomes, derived risk metrics, and candidate response pathways that support technical analysis beyond visual inspection. Current results demonstrate a functioning local truth pipeline in which scenario runs are executed through the backend, rendered through the web mission console, and reproduced through persisted artifact bundles that support run history, event navigation, scenario comparison, and metric export. Current validation results also show deterministic replay for fixed seed and fixed configuration cases, passing application programming interface tests for persistence and scenario execution, and numerical parity with seven reference scenario classes from the original runtime. In parallel, the platform now includes an implemented SSA workflow for conjunction screening and avoidance analysis with structured confidence and economic fields exposed in persisted outputs, although these decision fields remain preliminary and are not yet externally validated for operational use. This research remains in progress, with full ensemble uncertainty analysis, expanded debris ingestion, and a constrained policy and economic reasoning layer planned for next semester. The intent is to complete the integrated study and submit the resulting work for publication through the Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in September 2026.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Navigation, Guidance, Control and Dynamics Commons, Science and Technology Policy Commons, Space Vehicles Commons
Catalyst: A Unified Framework for Orbital Debris Risk, Conjunction Analysis, and Space Policy
Catalyst is an analyst grade mission console and offline simulation service that operationalizes the Embry Riddle MIT Orbital Capacity Assessment Toolbox Monte Carlo (E-MOCAT-MC) for Space Situational Awareness (SSA) aligned assessment, connecting scenario assumptions to time tagged events, quantitative stability indicators, and evidence preserving exports. E-MOCAT-MC serves as the authoritative generator of seeded scenario runs under explicit configuration control, ingesting locally served Two Line Element sets (TLEs) and persisting each run as a structured record containing configuration data, stochastic seeds, event logs, derived metrics, and replayable artifacts for deterministic analysis. Unlike systems that primarily visualize TLEs and object tracks, Catalyst is designed to compute reproducible scenario outcomes, derived risk metrics, and candidate response pathways that support technical analysis beyond visual inspection. Current results demonstrate a functioning local truth pipeline in which scenario runs are executed through the backend, rendered through the web mission console, and reproduced through persisted artifact bundles that support run history, event navigation, scenario comparison, and metric export. Current validation results also show deterministic replay for fixed seed and fixed configuration cases, passing application programming interface tests for persistence and scenario execution, and numerical parity with seven reference scenario classes from the original runtime. In parallel, the platform now includes an implemented SSA workflow for conjunction screening and avoidance analysis with structured confidence and economic fields exposed in persisted outputs, although these decision fields remain preliminary and are not yet externally validated for operational use. This research remains in progress, with full ensemble uncertainty analysis, expanded debris ingestion, and a constrained policy and economic reasoning layer planned for next semester. The intent is to complete the integrated study and submit the resulting work for publication through the Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in September 2026.