Self-Gravitating Scalar Field Configurations, Ultra Light Dark Matter and Galactic Scale Observations
Bihag Dave
TL;DR
This study investigates Ultra Light Dark Matter (ULDM) with spin-0 and mass around $m\sim 10^{-22}\,\text{eV}$, focusing on self-interactions characterized by $\lambda$. It leverages the nonrelativistic Gross-Pitaevskii-Poisson (GPP) framework to model stable soliton cores and explores how galactic-scale observations—central mass limits, rotation curves, and satellite tidal dynamics—constrain the ULDM mass and self-coupling in the $m$-$\lambda$ plane. The work shows that even tiny self-interactions, $|\lambda|\sim 10^{-90}-10^{-96}$, can significantly alter soliton properties and lifetimes, potentially reconciling ULDM with rotation curves (via repulsive SI) or extending satellite lifetimes (via attractive SI). It also demonstrates a machine-learning approach to infer density-profile and baryonic parameters from rotation curves, offering a data-driven path beyond traditional MCMC methods. Overall, the results underscore the pivotal role of self-interactions in ULDM phenomenology and showcase neural-network inference as a promising tool for galactic-scale DM parameter estimation.
Abstract
In this thesis, we investigate the possibility that dark matter consists of ultra light spin-zero particles with mass $m \sim 10^{-22}\ \text{eV}$. We focus on the role of self-interactions, assuming all other non-gravitational couplings to Standard Model particles are negligible. Such ultra light dark matter (ULDM) is expected to form stable self-gravitating scalar field configurations (solitons), whose properties depend on the particle mass and self-coupling $λ$. Using solutions of the Gross-Pitaevskii-Poisson equations, we explore how galactic-scale observations can constrain $m$ and $λ$. We show that observational upper limits on the mass enclosed in central galactic regions can probe both attractive and repulsive self-interactions with strengths $λ\sim \pm 10^{-96} - 10^{-95}$. We further demonstrate that self-interactions can allow ULDM to describe observed rotation curves as well as satisfy an empirical soliton-halo mass relation in low surface brightness galaxies for $m \sim 10^{-22}\ \text{eV}$ and $λ\gtrsim 10^{-90}$. We also study tidal effects in satellite dwarf galaxies and find that attractive self-interactions can extend their lifetimes over cosmological timescales, allowing ULDM to evade recent constraints derived for the non-interacting case. Finally, we explore machine learning based inference of dark matter and baryonic parameters from galaxy rotation curves, showing that neural networks can recover parameters consistent with observations.
