A profile likelihood analysis of the constrained MSSM with genetic algorithms

被引:65
作者
Akrami, Yashar [1 ]
Scott, Pat [1 ]
Edsjo, Joakim [1 ]
Conrad, Jan [1 ]
Bergstrom, Lars [1 ]
机构
[1] Stockholm Univ, Oskar Klein Ctr Cosmoparticle Phys, Dept Phys, SE-10691 Stockholm, Sweden
来源
JOURNAL OF HIGH ENERGY PHYSICS | 2010年 / 04期
基金
瑞典研究理事会;
关键词
Supersymmetry Phenomenology; NEUTRALINO DARK-MATTER; EVEN HIGGS BOSONS; RELIC DENSITY; MINIMAL SUPERGRAVITY; E(+)E(-) COLLISIONS; PROTON STABILITY; MODEL SELECTION; SCALAR LEPTONS; GAUGE-THEORY; MASS;
D O I
10.1007/JHEP04(2010)057
中图分类号
O412 [相对论、场论]; O572.2 [粒子物理学];
学科分类号
摘要
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 pro file 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.
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页数:46
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