Non-parametric Deprojection of Surface Brightness Profiles of Galaxies in Generalised Geometries
Dalia Chakrabarty

TL;DR
This paper introduces DOPING, a Bayesian non-parametric algorithm for 3D deprojection of galaxy surface brightness profiles in complex geometries, accounting for shape variations with radius using MCMC optimization.
Contribution
The paper presents a novel deprojection method that handles generalised galaxy geometries and shape changes, improving accuracy over traditional parametric approaches.
Findings
Successfully tested on galaxies with varying morphologies
Applied to dwarf elliptical galaxies and X-ray cluster images
Demonstrated effective shape and density recovery
Abstract
We present a new Bayesian non-parametric deprojection algorithm DOPING (Deprojection of Observed Photometry using and INverse Gambit), that is designed to extract 3-D luminosity density distributions from observed surface brightness maps , in generalised geometries, while taking into account changes in intrinsic shape with radius, using a penalised likelihood approach and an MCMC optimiser. We provide the most likely solution to the integral equation that represents deprojection of the measured to . In order to keep the solution modular, we choose to express as a function of the line-of-sight (LOS) coordinate . We calculate the extent of the system along the -axis, for a given point on the image that lies within an identified isophotal annulus. The extent along the LOS is binned and density is held a constant over each such -bin. The code begins…
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