Bose-Einstein Condensate dark matter with logarithmic nonlinearity

TL;DR

This paper explores a model of galactic dark matter as a Bose-Einstein condensate with logarithmic self-interactions, deriving its properties and testing its fit against observed galaxy rotation curves.

Contribution

It introduces a logarithmic nonlinearity in the condensate dark matter model and demonstrates its compatibility with galactic rotation curve data.

Findings

The model's rotation curves fit well with SPARC data.

The equation of state resembles that of an ideal gas.

The series solution describes the density distribution effectively.

Abstract

If dark matter is composed of massive bosons, a Bose-Einstein Condensation process must have occurred during the cosmological evolution. Therefore, galactic dark matter may be in a form of a self-gravitating condensate, in the presence of self-interactions. We consider the possibility that the self-interacting potential of the condensate dark matter is of the logarithmic form. In order to describe the condensate dark matter we use the Gross-Pitaevskii equation with a logarithmic nonlinearity, and the Thomas-Fermi approximation. With the use of the hydrodynamic representation of the Gross-Pitaevskii equation we obtain the equation of state of the condensate, which has the form of the ideal gas equation of state, with the pressure proportional to the dark matter density. The basic equation describing the density distribution of the static condensate is derived, and its solution is obtained in the form of a series solution, constructed with the help of the Adomian Decomposition Method. To test the model we consider the properties of the galactic rotation curves in the logarithmic Bose-Einstein Condensate dark matter scenario, by using a sample from the Spitzer Photometry and Accurate Rotation Curves (SPARC) data. The fit of the theoretical predictions of the rotation curves with the observational data indicate that the logarithmic Bose-Einstein Condensate dark matter model gives an acceptable description of the SPARC data, and thus it may be considered as a possible candidate for the in depth understanding of the dark matter properties.