Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations (2411.05596v1)

Abstract: State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges.

• The paper introduces a dual-model approach using XGBoost to predict temperature anomalies in Euclid telemetry.
• It leverages clustering and SHAP analysis to assess deviations across eleven temperature parameters and 35 covariate variables.
• The framework enables near-real-time anomaly detection, enhancing operational efficiency and reducing mission risks.

Machine Learning-Driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations

The dynamic operations of modern space missions, exemplified by the Euclid space telescope of the European Space Agency (ESA), necessitate sophisticated automation solutions due to their inherent complexity and the volume of data generated. This paper provides an analytical framework using machine learning techniques, specifically targeting the anomaly detection and forecasting within the telemetry data for the Euclid mission. The focus on temperature anomalies and their correlations with covariate parameters underscores the paper's applicability to optimizing the Euclid space telescope's operation and its scientific yield.

Machine learning methods such as eXtreme Gradient Boosting (XGBoost) are employed to enhance the capability of monitoring and predicting statuses from large telemetry datasets. This paper describes a dual-model approach. Initially, a predictive model is developed to estimate temperature based on historical data. This model is trained across eleven temperature parameters with varying time lags. An anomaly score is generated through clustering significant deviations in predicted temperatures from actual values, which marks potential aberrations in the telemetry data.

In tandem, a secondary model strives to elucidate the causes of these deviations by mapping them against 35 covariate parameters at different time intervals. The use of Shapley Additive Explanations (SHAP) in this context allows a granulated investigation into the impact of each parameter on anomalous predictions, providing insights into the telemetry's physical interactions and guiding potential interventions.

The results revealed patterns indicative of operational events within the spacecraft that impact temperature stability, such as the interaction between NISP internal temperatures, vis instrument operations, and solar aspect angles. The effectiveness of the developed models is illustrated by rapid computation times (approximately two minutes per parameter), suggesting practicality for near-real-time applications.

Further implications of this work involve enhancing operational robustness through early anomaly detection and potentially applying this framework to various ESA missions managing extensive telemetry datasets. The integration of such AI-driven methodologies promises to reduce operational risks significantly and enrich scientific outputs by maintaining optimal instrument conditions.

Moreover, the paper accentuates the importance of interpretable machine learning. While SHAP values provide insights into influential parameters, they do not inherently infer causality, which invites future research into causal inference mechanisms within telemetry contexts. Furthermore, this work paves the way for extrapolating these methods to other spacecraft systems, incorporating a broader range of telemetry data metrics such as spacecraft health diagnostics and operational regime indicators.

In conclusion, the implementation of machine learning strategies as developed for the Euclid mission represents a promising avenue for advancing space mission operations into higher efficiency and accuracy, serving as a paradigm for future missions across different scales and objectives. Such initiatives will undoubtedly contribute to the overarching goal of achieving deeper understanding and streamlined operations in the complex arena of space exploration.