A Dust spectral energy distribution model with hierarchical Bayesian inference. I. Formalism & benchmarking

TL;DR

This paper introduces HerBIE, a hierarchical Bayesian dust SED model that improves parameter recovery accuracy by accounting for realistic dust properties and physical condition mixing, tested through synthetic data.

Contribution

The paper presents a novel hierarchical Bayesian approach for full dust SED modeling, including realistic optical properties and stochastic heating, with comprehensive benchmarking.

Findings

Parameters are accurately recovered around true values.

No significant bias found even at low S/N ratios.

Model performance is robust across various observational conditions.

Abstract

This article presents a new dust SED model, named HerBIE, aimed at eliminating the noise-induced correlations and large scatter obtained when performing least-squares fits. The originality of this code is to apply the hierarchical Bayesian approach to full dust models, including realistic optical properties, stochastic heating and the mixing of physical conditions in the observed regions. We test the performances of our model by applying it to synthetic observations. We explore the impact on the recovered parameters of several effects: signal-to-noise ratio, SED shape, sample size, the presence of intrinsic correlations, the wavelength coverage and the use of different SED model components. We show that this method is very efficient: the recovered parameters are consistently distributed around their true values. We do not find any clear bias, even for the most degenerate parameters, or with extreme signal-to-noise ratios.