DOPING: a New Non-parametric Deprojection Scheme

TL;DR

DOPING is a novel non-parametric algorithm that reconstructs the three-dimensional luminosity density of astrophysical systems from surface brightness data, accommodating arbitrary shapes and orientations without assuming a fixed geometry.

Contribution

It introduces a flexible deprojection method that jointly estimates shape, inclination, and luminosity density using likelihood maximization, unlike previous fixed-geometry approaches.

Findings

Successfully reconstructs 3D luminosity densities from 2D data.

Handles arbitrary geometries and orientations.

Demonstrates effectiveness through tests and applications.

Abstract

We present a new non-parametric deprojection algorithm DOPING (Deprojection of Observed Photometry using and INverse Gambit), that is designed to extract the three dimensional luminosity density distribution $\rho$, from the observed surface brightness profile of an astrophysical system such as a galaxy or a galaxy cluster, in a generalised geometry, while taking into account changes in the intrinsic shape of the system. The observable is the 2-D surface brightness distribution of the system. While the deprojection schemes presented hitherto have always worked within the limits of an assumed intrinsic geometry, in DOPING, geometry and inclination can be provided as inputs. The $\rho$ that is most likely to project to the observed brightness data is sought; the maximisation of the likelihood is performed with the Metropolis algorithm. Unless the likelihood function is maximised, $\rho$ is tweaked in shape and amplitude, while maintaining positivity, but otherwise the luminosity distribution is allowed to be completely free-form. Tests and applications of the algorithm are discussed.