Parametric optimization with evolutionary strategies in particle physics

Berlich, Rüdiger; Kunze, M.

doi:10.1016/j.nima.2004.07.037

Cited by 3 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best knowledge of the author of this lecture, there is only one study of the ES applicability to high energy physics problems [13] in which the algorithm was tested for optimisation of event selection cuts and for parameter minimisation in a Dalitz plot analysis.…”

Section: Applications In High Energy Physicsmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Computation in High Energy Physics

Teodorescu

2008

Preprint

View full text Add to dashboard Cite

Evolutionary Computation is a branch of computer science with which, traditionally, High Energy Physics has fewer connections. Its methods were investigated in this field, mainly for data analysis tasks. These methods and studies are, however, less known in the high energy physics community and this motivated us to prepare this lecture. The lecture presents a general overview of the main types of algorithms based on Evolutionary Computation, as well as a review of their applications in High Energy Physics.

show abstract

Section: Applications In High Energy Physicsmentioning

confidence: 99%

“…GP was recently applied to event selection type problems [10,11,12]. ES were tested for optimisation of event selection criteria [13]. The first application of GEP to a high energy physics problem was presented in [14].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Computation in High Energy Physics

Teodorescu

2008

Preprint

View full text Add to dashboard Cite

show abstract

“…GP was recently applied to event selection type problems [17][18][19]. ES were tested for optimisation of event selection criteria [20].…”

Section: Introductionmentioning

confidence: 99%

High Energy Physics event selection with Gene Expression Programming

Teodorescu

Sherwood

2008

Computer Physics Communications

177

View full text Add to dashboard Cite

A profile likelihood analysis of the constrained MSSM with genetic algorithms

Akrami

Scott

Edsjö

et al. 2010

J. High Energ. Phys.

View full text Add to dashboard Cite

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.

show abstract

Parametric optimization with evolutionary strategies in particle physics

Cited by 3 publications

References 1 publication

Evolutionary Computation in High Energy Physics

Evolutionary Computation in High Energy Physics

High Energy Physics event selection with Gene Expression Programming

A profile likelihood analysis of the constrained MSSM with genetic algorithms

Contact Info

Product

Resources

About