Identifying diffuse spatial structures in high-energy photon lists. (arXiv:2208.07427v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Fan_M/0/1/0/all/0/1">Minjie Fan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Wang_J/0/1/0/all/0/1">Jue Wang</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kashyap_V/0/1/0/all/0/1">Vinay L. Kashyap</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Lee_T/0/1/0/all/0/1">Thomas C. M. Lee</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Dyk_D/0/1/0/all/0/1">David A. van Dyk</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zezas_A/0/1/0/all/0/1">Andreas Zezas</a>
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.
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.
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