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Launch Service Provider (LSP) Analytics

Showcased at the Defense TechConnect Summit, LSP Analytics converges a monotonic gradient boosting algorithm with an LLM to enhance probabilistic risk assessments in launch vehicle processing operations.

Kyle Lyon
Kyle Lyon
3 min read
Launch Service Provider (LSP) Analytics

In the high-stakes arena of National Security Space Launch mission risk, an innovative approach is changing the way the U.S. Space Force addresses the challenges of launch vehicle ground operations. This innovative method, which I had the privilege to present at the Defense TechConnect Innovation Summit & Expo, combines the precision of machine learning with the depth of natural language processing, enhancing risk assessment—a critical component of the Space Force's Assured Access to Space mission.

Background and Motivation

The genesis of this approach lies in the DoD's tumultuous history with launch failures, particularly in the late 1990s. Launch-site processing errors led to multiple failures, costing over $3 billion and an immeasurable loss in capabilities.

This necessitated a paradigm shift, culminating in the Mission Assurance (MA) program, which emphasizes a data-driven approach over subjective risk models. The objective? To identify and mitigate risks in design, production, and testing to ensure mission success. Integral to this mission are the 5th and 2nd Space Launch Squadrons, who, despite growing workloads and complexities from an expanding roster of Launch Service Providers (LSPs) like ULA, Blue Origin, and SpaceX, are not projected to receive increased manpower. This underscores the need for efficient, accurate risk assessment tools in a rapidly evolving technological landscape.

Technical Approach

The heart of this methodology lies in the synthesis of Monotonic Gradient Boosting and target encoding. This technique is not mere data manipulation; it's a careful translation of categorical elements into risk-weighted numerical representations, aligned with real-world risk patterns through human-set monotonicity constraints. This not only enhances predictive accuracy but also ensures the model's decisions are understandable and transparent, a crucial factor when dealing with complex risk assessments in space launches.

Data and Methods

Using data from SpaceX's internal issue tracking system, the model employs advanced preprocessing techniques like additive smoothing, recursive feature elimination, and synthetic minority oversampling. This not only bolsters model robustness but also enhances its statistical reliability.


The model demonstrates its efficacy with a 64% capture rate in identifying major risks and a ROC curve area of 0.96. The use of capture rate, particularly in the context of a 9:1 class imbalance, is a strategic choice. It reflects the model's ability to prioritize and effectively identify critical risks.

Enhancing Qualitative Analysis

Beyond mere classification, the system can leverage Large Language Models fine-tuned for department-specific sentiment analysis. This adds a qualitative layer to the data, enabling the system to interpret the context and engineering rationales within textual data. This transcends traditional quantitative assessments, bridging the gap between raw data and the subtleties of human expertise and intuition.

Future Directions

The path ahead involves refining anomaly detection algorithms to monitor data streams for atypical patterns and accelerate LLM sentiment analysis and classification. This will further enhance the system's efficiency and effectiveness in risk assessment for space launches.

Kyle Lyon Twitter

I'm Kyle, a Data Scientist in DevSecOps contracting with the U.S. Space Force through Silicon Mountain Technologies.