XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images
This addresses the lack of annotated data for training machine learning models to detect artefacts in astronomical observations, which is an incremental improvement for astronomers dealing with large datasets.
The authors tackled the problem of detecting artefacts in astronomical images by creating a benchmark dataset of 1000 hand-annotated images from the XMM-Newton telescope and developing a hybrid instance segmentation method, achieving accurate detection and masking.
Reflected or scattered light produce artefacts in astronomical observations that can negatively impact the scientific study. Hence, automated detection of these artefacts is highly beneficial, especially with the increasing amounts of data gathered. Machine learning methods are well-suited to this problem, but currently there is a lack of annotated data to train such approaches to detect artefacts in astronomical observations. In this work, we present a dataset of images from the XMM-Newton space telescope Optical Monitoring camera showing different types of artefacts. We hand-annotated a sample of 1000 images with artefacts which we use to train automated ML methods. We further demonstrate techniques tailored for accurate detection and masking of artefacts using instance segmentation. We adopt a hybrid approach, combining knowledge from both convolutional neural networks (CNNs) and transformer-based models and use their advantages in segmentation. The presented method and dataset will advance artefact detection in astronomical observations by providing a reproducible baseline. All code and data are made available (https://github.com/ESA-Datalabs/XAMI-model and https://github.com/ESA-Datalabs/XAMI-dataset).