Exploring X-ray variability with unsupervised machine learning I. Self-organizing maps applied to XMM-Newton data

TL;DR

This paper demonstrates how self-organizing maps can effectively cluster and visualize X-ray light curve data from XMM-Newton, aiding in source characterization and anomaly detection in large datasets.

Contribution

It introduces the application of self-organizing maps to X-ray light curves, enabling efficient clustering and visualization of over 100,000 sources for the first time.

Findings

Identified distinct clusters associated with flares, eclipses, and dips.

Reduced manual effort in source characterization by orders of magnitude.

Highlighted issues with simple temporal model fitting in noisy data.

Abstract

XMM-Newton provides unprecedented insight into the X-ray Universe, recording variability information for hundreds of thousands of sources. Manually searching for interesting patterns in light curves is impractical, requiring an automated data-mining approach for the characterization of sources. Straightforward fitting of temporal models to light curves is not a sure way to identify them, especially with noisy data. We used unsupervised machine learning to distill a large data set of light-curve parameters, revealing its clustering structure in preparation for anomaly detection and subsequent searches for specific source behaviors (e.g., flares, eclipses). Self-organizing maps (SOMs) achieve dimensionality reduction and clustering within a single framework. They are a type of artificial neural network trained to approximate the data with a two-dimensional grid of discrete interconnected units, which can later be visualized on the plane. We trained our SOM on temporal-only parameters computed from more than 100,000 detections from the EXTraS catalog. The resulting map reveals that about 2500 most variable sources are clustered based on temporal characteristics. We find distinctive regions of the SOM map associated with flares, eclipses, dips, linear light curves, and others. Each group contains sources that appear similar by eye. We single out a handful of interesting sources for further study. The condensed view of our dataset provided by SOMs allowed us to identify groups of similar sources, speeding up manual characterization by orders of magnitude. Our method also highlights problems with fitting simple temporal models to light curves and can be used to mitigate them to an extent. This will be crucial for fully exploiting the high data volume expected from upcoming X-ray surveys, and may also help with interpreting supervised classification models.