BayesCLUMPY: Bayesian Inference with Clumpy Dusty Torus Models

TL;DR

This paper introduces a fast Bayesian inference framework combining machine learning and sampling methods to analyze clumpy dusty torus models in active galactic nuclei, significantly reducing computational costs.

Contribution

It presents a novel approach using neural networks to interpolate model databases, enabling efficient Bayesian inference of torus parameters from infrared observations.

Findings

Neural network approximation reduces database size by 10,000 times.

Inference provides confidence intervals for key torus parameters.

Application to Centaurus A demonstrates effective parameter constraints.

Abstract

Our aim is to present a fast and general Bayesian inference framework based on the synergy between machine learning techniques and standard sampling methods and apply it to infer the physical properties of clumpy dusty torus using infrared photometric high spatial resolution observations of active galactic nuclei. We make use of the Metropolis-Hastings Markov Chain Monte Carlo algorithm for sampling the posterior distribution function. Such distribution results from combining all a-priori knowledge about the parameters of the model and the information introduced by the observations. The main difficulty resides in the fact that the model used to explain the observations is computationally demanding and the sampling is very time consuming. For this reason, we apply a set of artificial neural networks that are used to approximate and interpolate a database of models. As a consequence, models not present in the original database can be computed ensuring continuity. We focus on the application of this solution scheme to the recently developed public database of clumpy dusty torus models. The machine learning scheme used in this paper allows us to generate any model from the database using only a factor 10^-4 of the original size of the database and a factor 10^-3 in computing time. The posterior distribution obtained for each model parameter allows us to investigate how the observations constrain the parameters and which ones remain partially or completely undetermined, providing statistically relevant confidence intervals. As an example, the application to the nuclear region of Centaurus A shows that the optical depth of the clouds, the total number of clouds and the radial extent of the cloud distribution zone are well constrained using only 6 filters.