Identifying diffuse spatial structures in high-energy photon lists

TL;DR

This paper introduces SRGonG, a novel non-parametric algorithm that segments high-energy photon data directly from event lists to identify diffuse and point sources without binning, validated through simulations and real Chandra data.

Contribution

The paper presents SRGonG, a new graph-based segmentation method for photon event lists that effectively detects diffuse and point sources without relying on image binning.

Findings

Successfully identifies irregular low-brightness emission structures.

Effectively segments complex astrophysical data like the Antennae galaxies.

Performs comparably to traditional methods in source detection.

Abstract

Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel non-parametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing, that we call Seeded Region Growing on Graph, after which the overall method is named SRGonG. Starting with a set of seed locations, this results in an over-segmented dataset, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG's use on the Chandra data of the Antennae galaxies, and show that it segments the complex structures appropriately.