Modeling Globular Cluster Counts with Bayesian Latent Models

TL;DR

This paper introduces a Bayesian latent model for globular cluster counts, incorporating measurement errors and improving computational efficiency, with code available on GitHub.

Contribution

It extends previous models by adding a Gaussian observation layer and enhances computational tractability for globular cluster population analysis.

Findings

The model accurately captures the scaling relation between GC counts and galaxy mass.

Incorporating measurement errors improves the model's reliability.

The implementation demonstrates efficient Bayesian inference for astrophysical count data.

Abstract

We present a Bayesian latent model to describe the scaling relation between globular cluster populations and their host galaxies, updating the framework proposed in de Souza 2015. GC counts are drawn from a negative-binomial (NB) process linked to host stellar mass, augmented with a newly introduced Gaussian observation layer that enables efficient propagation of measurement errors. The revised formulation preserves the underlying NB process while improving computational tractability. The code snippets, implemented in Nimble and PyMC are released under the MIT license at https://github.com/COINtoolbox/Generalized-Linear-Models-Tutorial/blob/master/Count/readme.md