Bayesian analysis for rotational curves with $\ell$-boson stars as a dark matter component

TL;DR

This paper uses Bayesian analysis of rotational curves from Low Brightness Surface Galaxies to infer parameters of $\

Contribution

It introduces a Bayesian framework for analyzing $\\ell$-boson star dark matter models, including multi-state solutions, and compares their effectiveness using model selection criteria.

Findings

Multi-state $\\ell$-boson star models are favored by most galaxy data.

Scalar field mass is slightly larger in multi-state models.

Bayesian analysis effectively constrains dark matter parameters.

Abstract

Using Low Brightness Surface Galaxies (LBSG) rotational curves we inferred the free parameters of $\ell$-boson stars as a dark matter component. The $\ell$-boson stars are numerical solutions to the non-relativistic limit of the Einstein-Klein-Gordon system, the Schr\"odinger-Poisson (SP) system. These solutions are parametrized by an angular momentum number $\ell = (N-1)/2$ and an excitation number $n$. We perform a bayesian analysis by modifying the SimpleMC code to perform the parameter inference, for the cases with $\ell = 0$, $\ell = 1$ and multi-states of $\ell$-boson stars. We used the Akaike information criterion (AIC), Bayesian information criterion and the Bayes factor to compare the excited state ($\ell$=1) and the multi-state case with the ground state ($\ell$=0) as the base model due to its simplicity. We found that the data in most galaxies in the sample favours the multi-states case and that the scalar field mass tends to be slightly bigger than the ground state case.