Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations

• URL: http://arxiv.org/abs/2411.05596v1
• Date: Fri, 08 Nov 2024 14:31:50 GMT
• Title: Machine learning-driven Anomaly Detection and Forecasting for Euclid Space Telescope Operations
• Authors: Pablo Gómez, Roland D. Vavrek, Guillermo Buenadicha, John Hoar, Sandor Kruk, Jan Reerink,
• Abstract summary: State-of-the-art space science missions increasingly rely on automation. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions.
• Score: 0.0
• License:
• 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.

Related papers

• Causal Representation Learning in Temporal Data via Single-Parent Decoding [66.34294989334728]
  Scientific research often seeks to understand the causal structure underlying high-level variables in a system. Scientists typically collect low-level measurements, such as geographically distributed temperature readings. We propose a differentiable method, Causal Discovery with Single-parent Decoding, that simultaneously learns the underlying latents and a causal graph over them.
  arXiv  Detail & Related papers  (2024-10-09T15:57:50Z)
• Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
  We present a machine learning framework that performs complete BNS inference in just one second without making any approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
  arXiv  Detail & Related papers  (2024-07-12T18:00:02Z)
• Smoke and Mirrors in Causal Downstream Tasks [59.90654397037007]
  This paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations. We compare 6 480 models fine-tuned from state-of-the-art visual backbones, and find that the sampling and modeling choices significantly affect the accuracy of the causal estimate. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones.
  arXiv  Detail & Related papers  (2024-05-27T13:26:34Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through Feature Learning [0.7388859384645262]
  We present a demonstration of a method that can detect and characterize emergent transient anomalies of massively complex systems. One of the prevalent issues limiting gravitational-wave discoveries is the noise artifacts of terrestrial origin. We show how a highly interpretable convolutional classifier can automatically learn to detect transient anomalies from auxiliary detector data.
  arXiv  Detail & Related papers  (2022-03-09T23:39:41Z)
• Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets [0.7388859384645262]
  We study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector. We introduce models that reduce the error rate by over 60% compared to the previous state of the art.
  arXiv  Detail & Related papers  (2022-02-27T23:41:23Z)
• Encoding Causal Macrovariables [0.0]
  In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. This work introduces a novel algorithmic approach that is inspired by a new characterisation of causal macrovariables as information bottlenecks between microstates.
  arXiv  Detail & Related papers  (2021-11-29T17:25:11Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Graph Neural Network-Based Anomaly Detection in Multivariate Time Series [17.414474298706416]
  We develop a new way to detect anomalies in high-dimensional time series data. Our approach combines a structure learning approach with graph neural networks. We show that our method detects anomalies more accurately than baseline approaches.
  arXiv  Detail & Related papers  (2021-06-13T09:07:30Z)
• Unsupervised machine learning of topological phase transitions from experimental data [52.77024349608834]
  We apply unsupervised machine learning techniques to experimental data from ultracold atoms. We obtain the topological phase diagram of the Haldane model in a completely unbiased fashion. Our work provides a benchmark for unsupervised detection of new exotic phases in complex many-body systems.
  arXiv  Detail & Related papers  (2021-01-14T16:38:21Z)
• Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling [8.896888286819635]
  Systems exhibiting chaos are ubiquitous across Earth Sciences. System Identification remains a challenge in climate science. We consider a chaotic system - two-level Lorenz-96 - used as a benchmark model in the climate science literature.
  arXiv  Detail & Related papers  (2020-08-12T22:37:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.