H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations

• URL: http://arxiv.org/abs/2509.14472v1
• Date: Wed, 17 Sep 2025 23:04:55 GMT
• Title: H-Alpha Anomalyzer: An Explainable Anomaly Detector for Solar H-Alpha Observations
• Authors: Mahsa Khazaei, Azim Ahmadzadeh, Alexei Pevtsov, Luca Bertello, Alexander Pevtsov,
• Abstract summary: In this study, we introduce a lightweight (non-ML) anomaly-detection algorithm, called H-Alpha Anomalyzer.<n>Unlike many black-box algorithms, our approach highlights exactly which regions triggered the anomaly flag and quantifies the corresponding anomaly likelihood.<n>Our results demonstrate that the proposed model not only outperforms existing methods but also provides explainability.
• Score: 37.56003520528009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The plethora of space-borne and ground-based observatories has provided astrophysicists with an unprecedented volume of data, which can only be processed at scale using advanced computing algorithms. Consequently, ensuring the quality of data fed into machine learning (ML) models is critical. The H$\alpha$ observations from the GONG network represent one such data stream, producing several observations per minute, 24/7, since 2010. In this study, we introduce a lightweight (non-ML) anomaly-detection algorithm, called H-Alpha Anomalyzer, designed to identify anomalous observations based on user-defined criteria. Unlike many black-box algorithms, our approach highlights exactly which regions triggered the anomaly flag and quantifies the corresponding anomaly likelihood. For our comparative analysis, we also created and released a dataset of 2,000 observations, equally divided between anomalous and non-anomalous cases. Our results demonstrate that the proposed model not only outperforms existing methods but also provides explainability, enabling qualitative evaluation by domain experts.

Related papers

• Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models [27.008700759998945]
  We propose VELM, a novel pipeline for anomaly classification.<n>We introduce MVTec-AC and VisA-AC, refined versions of the widely used MVTec-AD and VisA-AC datasets.<n>Our approach achieves a state-of-the-art anomaly classification accuracy of 80.4% on MVTec-AD, exceeding the prior baselines by 5%, and 84% on MVTec-AC, demonstrating the effectiveness of VELM.
  arXiv  Detail & Related papers  (2025-05-05T13:08:25Z)
• Unsupervised anomaly detection in large-scale estuarine acoustic telemetry data [0.3495246564946556]
  We present guidelines for data pre-processing, resampling strategies, labelling process, feature engineering, data splitting methodologies, and the selection and interpretation of machine learning and deep learning models for anomaly detection.<n>Among the evaluated models, neural networks autoencoder (NN-AE) demonstrated superior performance, aided by our proposed threshold-finding algorithm.
  arXiv  Detail & Related papers  (2025-02-03T17:23:29Z)
• Abnormal component analysis [9.387706860375461]
  We introduce a new statistical tool dedicated for exploratory analysis of abnormal observations using data depth as a score.<n>Abnormal component analysis (shortly ACA) is a method that searches a low-dimensional data representation that best visualises and explains anomalies.
  arXiv  Detail & Related papers  (2023-12-26T17:57:46Z)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach [49.995833831087175]
  This work proposes a novel method for generating generic Video-temporal PAs by inpainting a masked out region of an image. In addition, we present a simple unified framework to detect real-world anomalies under the OCC setting. Our method performs on par with other existing state-of-the-art PAs generation and reconstruction based methods under the OCC setting.
  arXiv  Detail & Related papers  (2023-11-27T13:14:06Z)
• Weakly-Supervised Anomaly Detection in the Milky Way [1.3375143521862154]
  We use Classification Without Labels (CWoLa) to identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labeled streams or knowledge of astrophysical principles. This technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.
  arXiv  Detail & Related papers  (2023-05-05T18:00:09Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• New Methods and Datasets for Group Anomaly Detection From Fundamental Physics [0.4297070083645048]
  Unsupervised group anomaly detection has become a new frontier of fundamental physics. We propose a realistic synthetic benchmark dataset (LHCO 2020) for the development of group anomaly detection algorithms.
  arXiv  Detail & Related papers  (2021-07-06T18:00:57Z)
• Bump Hunting in Latent Space [0.0]
  We introduce a physics inspired variational autoencoder (VAE) architecture which performs competitively and robustly on the LHC Olympics Machine Learning Challenge datasets. We demonstrate how embedding some physical observables directly into the VAE latent space, while at the same time keeping the classifier manifestly agnostic to them, can help to identify and features in measured spectra as caused by the presence of anomalies in a dataset.
  arXiv  Detail & Related papers  (2021-03-11T10:48:10Z)

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.