$Q$-balls, neural networks and galaxy rotation curves
Alexandre M. Pombo, Lorenzo Pizzuti, Alessandra di Giacomo

TL;DR
This paper explores whether rotating scalar Q-balls, modeled with advanced numerical methods, can serve as dark matter halos to reproduce galaxy rotation curves, showing promising agreement with observations and standard profiles.
Contribution
It introduces a hybrid numerical framework combining spectral methods and neural networks to construct and analyze rotating Q-ball solutions fitting galaxy rotation data.
Findings
Good fit to observed galaxy rotation curves
Comparable performance to standard dark matter profiles
Estimated scalar field particle mass around 10^{-27} eV
Abstract
Can a dynamically robust (\textit{aka} stable) -ball reproduce the rotation curve of a disk galaxy? In an astrophysical environment, -balls are non-topological solitons that are transparent and only perceived by their gravitational effects. Traditionally, scalar -balls are modelled with a polynomial potential, but axion-like periodic potentials are also expected to support such solitonic configurations. In the presence of angular momentum, -balls acquire a toroidal structure with a central density void, qualitatively resembling the axially-symmetric structure of disk galaxies. Motivated by this similarity, we investigate whether rotating scalar -balls can reproduce the observed rotation curves of disk galaxies. In this work, we use a recently developed hybrid numerical framework that combines a high-accuracy pseudo-spectral method with a physics-informed neural network…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Cosmology and Gravitation Theories · Astronomy and Astrophysical Research
