Supersymmetry vis-\`a-vis Observation: Dark Matter Constraints, Global Fits and Statistical Issues

TL;DR

This paper reviews supersymmetry as a dark matter candidate, discusses observational constraints from astrophysics and collider data, and highlights statistical challenges in global fits, proposing genetic algorithms as a solution.

Contribution

It introduces a comprehensive analysis of supersymmetric models with a focus on dark matter constraints and proposes genetic algorithms to improve parameter space scanning.

Findings

Gamma-ray observations constrain supersymmetric dark matter models.

Future direct detection experiments will enhance parameter estimation.

Genetic algorithms outperform traditional methods in global fits.

Abstract

Weak-scale supersymmetry is one of the most favoured theories beyond the Standard Model of particle physics that elegantly solves various theoretical and observational problems in both particle physics and cosmology. In this thesis, I describe the theoretical foundations of supersymmetry, issues that it can address and concrete supersymmetric models that are widely used in phenomenological studies. I discuss how the predictions of supersymmetric models may be compared with observational data from both colliders and cosmology. I show why constraints on supersymmetric parameters by direct and indirect searches of particle dark matter are of particular interest in this respect. Gamma-ray observations of astrophysical sources, in particular dwarf spheroidal galaxies, by the Fermi satellite, and recording nuclear recoil events and energies by future ton-scale direct detection experiments are shown to provide powerful tools in searches for supersymmetric dark matter and estimating supersymmetric parameters. I discuss some major statistical issues in supersymmetric global fits to experimental data. In particular, I further demonstrate that existing advanced scanning techniques may fail in correctly mapping the statistical properties of the parameter spaces even for the simplest supersymmetric models. Complementary scanning methods based on Genetic Algorithms are proposed.