ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections

• URL: http://arxiv.org/abs/2505.09365v1
• Date: Wed, 14 May 2025 13:17:45 GMT
• Title: ARCANE -- Early Detection of Interplanetary Coronal Mass Ejections
• Authors: H. T. Rüdisser, G. Nguyen, J. Le Louëdec, C. Möstl,
• Abstract summary: ARCANE is the first framework explicitly designed for early ICME detection in streaming solar wind data.<n>We find that our detection pipeline achieves an F1 score of 0.53, with an average detection delay of 21.5% of the event's duration.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interplanetary coronal mass ejections (ICMEs) are major drivers of space weather disturbances, posing risks to both technological infrastructure and human activities. Automatic detection of ICMEs in solar wind in situ data is essential for early warning systems. While several methods have been proposed to identify these structures in time series data, robust real-time detection remains a significant challenge. In this work, we present ARCANE - the first framework explicitly designed for early ICME detection in streaming solar wind data under realistic operational constraints, enabling event identification without requiring observation of the full structure. Our approach evaluates the strengths and limitations of detection models by comparing a machine learning-based method to a threshold-based baseline. The ResUNet++ model, previously validated on science data, significantly outperforms the baseline, particularly in detecting high-impact events, while retaining solid performance on lower-impact cases. Notably, we find that using real-time solar wind (RTSW) data instead of high-resolution science data leads to only minimal performance degradation. Despite the challenges of operational settings, our detection pipeline achieves an F1 score of 0.53, with an average detection delay of 21.5% of the event's duration while only seeing a minimal amount of data. As more data becomes available, the performance increases significantly. These results mark a substantial step forward in automated space weather monitoring and lay the groundwork for enhanced real-time forecasting capabilities.

Related papers

• Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better [63.567886330598945]
  Infrared small target (IRST) detection is challenging in simultaneously achieving precise, universal, robust and efficient performance.<n>Current learning-based methods attempt to leverage more" information from both the spatial and the short-term temporal domains.<n>We propose an efficient deep temporal probe network (DeepPro) that only performs calculations in the time dimension for IRST detection.
  arXiv  Detail & Related papers  (2025-06-15T08:19:32Z)
• Leveraging Satellite Image Time Series for Accurate Extreme Event Detection [5.105561029577617]
  Climate change is leading to an increase in extreme weather events, causing significant environmental damage and loss of life.<n>We propose SITS-Extreme, a novel framework that leverages satellite image time series to detect extreme events by incorporating multiple pre-disaster observations.
  arXiv  Detail & Related papers  (2025-06-13T07:58:20Z)
• Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix [1.15692661299731]
  Enhancing robustness of object detection systems under adverse weather conditions is crucial for the advancement of autonomous driving technology.<n>This study presents a novel approach leveraging the diffusion model Instruct Pix2Pix to generate realistic datasets with weather-based augmentations.
  arXiv  Detail & Related papers  (2025-05-13T05:12:07Z)
• Research, Applications and Prospects of Event-Based Pedestrian Detection: A Survey [10.494414329120909]
  Event-based cameras, inspired by the biological retina, have evolved into cutting-edge sensors distinguished by their minimal power requirements, negligible latency, superior temporal resolution, and expansive dynamic range. Event-based cameras address limitations by eschewing extraneous data transmissions and obviating motion blur in high-speed imaging scenarios. This paper offers an exhaustive review of research and applications particularly in the autonomous driving context.
  arXiv  Detail & Related papers  (2024-07-05T06:17:00Z)
• Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method [66.80344502790231]
  We extend meteorological downscaling to arbitrary scattered station scales and establish a new benchmark and dataset. Inspired by data assimilation techniques, we integrate observational data into the downscaling process, providing multi-scale observational priors. Our proposed method outperforms other specially designed baseline models on multiple surface variables.
  arXiv  Detail & Related papers  (2024-01-22T14:02:56Z)
• Enhancing Lidar-based Object Detection in Adverse Weather using Offset Sequences in Time [1.1725016312484975]
  Lidar-based object detection is significantly affected by adverse weather conditions such as rain and fog. Our research provides a comprehensive study of effective methods for mitigating the effects of adverse weather on the reliability of lidar-based object detection.
  arXiv  Detail & Related papers  (2024-01-17T08:31:58Z)
• Efficient Real-time Smoke Filtration with 3D LiDAR for Search and Rescue with Autonomous Heterogeneous Robotic Systems [56.838297900091426]
  Smoke and dust affect the performance of any mobile robotic platform due to their reliance on onboard perception systems. This paper proposes a novel modular computation filtration pipeline based on intensity and spatial information.
  arXiv  Detail & Related papers  (2023-08-14T16:48:57Z)
• Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence [3.3810628880631226]
  This work presents a novel methodology to automate the creation of datasets for the detection of target events. The presented approach first processes the raw data by applying a pipeline consisting of spatial band registration and georeferencing. It detects the target events by leveraging event-specific state-of-the-art algorithms on the Level-1C products. We apply the proposed methodology to realize THRawS (Thermal Hotspots in Raw Sentinel-2 data), the first dataset of Sentinel-2 raw data containing warm thermal hotspots.
  arXiv  Detail & Related papers  (2023-05-12T09:54:21Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data [0.0]
  We propose a pipeline for the automatic detection of ICMEs using a method that has recently proven successful in medical image segmentation. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes.
  arXiv  Detail & Related papers  (2022-05-07T07:25:05Z)
• 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)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)

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.