XAMI -- A Benchmark Dataset for Artefact Detection in XMM-Newton Optical Images

TL;DR

This paper introduces XAMI, a new annotated dataset of XMM-Newton optical images for artefact detection, and presents a hybrid ML approach using CNNs and transformers for accurate artefact segmentation.

Contribution

The work provides the first annotated dataset for artefact detection in XMM-Newton images and develops a hybrid ML method combining CNNs and transformers for improved segmentation.

Findings

The dataset contains 1000 hand-annotated images with artefacts.

The hybrid CNN-transformer model achieves accurate artefact detection.

Code and data are publicly available for reproducibility.

Abstract

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).