Kavli Affiliate: Steven W. Allen
| First 5 Authors: Maya Benyas, Jordan Pfeifer, Adam B. Mantz, Steven W. Allen, Elise Darragh-Ford
| Summary:
The X-ray morphologies of clusters of galaxies display significant
variations, reflecting their dynamical histories and the nonlinear dependence
of X-ray emissivity on the density of the intracluster gas. Qualitative and
quantitative assessments of X-ray morphology have long been considered a proxy
for determining whether clusters are dynamically active or "relaxed."
Conversely, the use of circularly or elliptically symmetric models for cluster
emission can be complicated by the variety of complex features realized in
nature, spanning scales from Mpc down to the resolution limit of current X-ray
observatories. In this work, we use mock X-ray images from simulated clusters
from THE THREE HUNDRED project to define a basis set of cluster image features.
We take advantage of clusters’ approximate self similarity to minimize the
differences between images before encoding the remaining diversity through a
distribution of high order polynomial coefficients. Principal component
analysis then provides an orthogonal basis for this distribution, corresponding
to natural perturbations from an average model. This representation allows
novel, realistically complex X-ray cluster images to be easily generated, and
we provide code to do so. The approach provides a simple way to generate
training data for cluster image analysis algorithms, and could be
straightforwardly adapted to generate clusters displaying specific types of
features, or selected by physical characteristics available in the original
simulations.
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