A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms

TL;DR

This paper introduces a new genetic algorithm-based method for scanning the CMSSM parameter space, revealing high-likelihood regions often missed by traditional techniques, thus impacting statistical inferences in supersymmetry research.

Contribution

A novel genetic algorithm approach optimized for profile likelihood analysis in the CMSSM, improving detection of high-likelihood parameter regions compared to existing methods.

Findings

Many high-likelihood points in CMSSM are missed by traditional scans.

The best-fit point is in the focus point region.

High-mass regions are more thoroughly explored with the new method.

Abstract

The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.