Higher-order multipole amplitude measurement in ψ ′→γχ c2

Using 106×106 ψ ′ events collected with the BESIII detector at the BEPCII storage ring, the higher-order multipole amplitudes in the radiative transition ψ ′→γχ c2→γπ +π -/γK +K - are measured. A fit to the χ c2 production and decay angular distributions yields M2=0.046±0. 010±0.013 and E3=0.015±0.008±0.018, where the first errors are statistical and the second systematic. Here M2 denotes the normalized magnetic quadrupole amplitude and E3 the normalized electric octupole amplitude. This measurement shows evidence for the existence of the M2 signal with 4.4σ statistical significance and is consistent with the charm quark having no anomalous magnetic moment. © 2011 American Physical Society.published_or_final_versio

Determination of the number of J/ψ events with J/ψ → inclusive decays

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Two-photon widths of the χ c0,2 states and helicity analysis for χ c2→γγ

Based on a data sample of 106×106 ψ ′ events collected with the BESIII detector, the decays ψ ′→γχ c0,2, χ c0,2→γγ are studied to determine the two-photon widths of the χ c0,2 states. The two-photon decay branching fractions are determined to be B(χ c0→γγ)=(2. 24±0.19±0.12±0.08)×10 -4 and B(χ c2→γγ)=(3.21±0.18±0. 17±0.13)×10 -4. From these, the two-photon widths are determined to be Γ γγ(χ c0)=(2. 33±0.20±0.13±0.17)keV, Γ γγ(χ c2)=(0.63±0.04±0. 04±0.04)keV, and R=Γ γγ(χ c2)/ Γ γγ(χ c0)=0.271±0. 029±0.013±0.027, where the uncertainties are statistical, systematic, and those from the PDG B(ψ ′→γχ c0,2) and Γ(χ c0,2) errors, respectively. The ratio of the two-photon widths for helicity λ=0 and helicity λ=2 components in the decay χ c2→γγ is measured for the first time to be f 0/2=Γγγλ= 0(χ c2)/Γγγλ=2(χ c2)=0. 00±0.02±0.02. © 2012 American Physical Society.published_or_final_versio

Search for a light exotic particle in J/psi radiative decays

Using a data sample containing 1.06x10^8 psi' events collected with the BESIII detector at the BEPCII electron-positron collider, we search for a light exotic particle X in the process psi' -> pi^+ pi^- J/psi, J/psi -> gamma X, X -> mu^+ mu^-. This light particle X could be a Higgs-like boson A^0, a spin-1 U boson, or a pseudoscalar sgoldstino particle. In this analysis, we find no evidence for any mu^+mu^- mass peak between the mass threshold and 3.0 GeV/c^2. We set 90%-confidence-level upper limits on the product-branching fractions for J/psi -> gamma A^0, A^0 -> mu^+ mu^- which range from 4x10^{-7} to 2.1x10^{-5}, depending on the mass of A^0, for M(A^0)<3.0 GeV/c^2. Only one event is seen in the mass region below 255 MeV/c^2 and this has a mu^+mu^- mass of 213.3 MeV/c^2 and the product branching fraction upper limit 5x10^{-7}.Comment: 7 pages, 3 figures, submitted to Physical Review

Applications of Genetic Programming to Finance and Economics: Past, Present, Future

While the origins of Genetic Programming (GP) stretch back over fifty years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the Genetic Programming Bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics

Adaptace v genetickém programování a symbolické regresi

This paper focuses on the use of hybrid genetic programming for the supervised machine learning method called symbolic regression. While the basic version of GP symbolic regression optimizes both the model structure and its parameters, the hybrid version can use genetic programming to find the model structure. Consequently, local learning is used to tune model parameters. Such tuning of parameters represents the lifetime adaptation of individuals. Choice of local learning method can accelerate the evolution, but it also has its disadvantages in the form of additional costs. Strong local learning can inhibit the evolutionary search for the optimal genotype due to the hiding effect, in which the fitness of the individual only slightly depends on his inherited genes. This paper aims to compare the Lamarckian and Baldwinian approaches to the lifetime adaptation of individuals and their influence on the rate of evolution in the search for function, which fits the given input-output data.Tento článek se zaměřuje na použití hybridního genetického programování pro symbolickou regresi Hybridní verze symbolické regrese používá genetické programování pro hledání struktury matematického modelu a lokální učení pro ladění parametrů modelu. Lokálního učení může urychlit evoluci, ale nevýhodou jsou dodatečné výpočetní náklady. Článek porovnává Lamarckův a Baldwinův přístup k evoluci a jejich vliv na umělou evoluci při hledání matematického modelu popisujícího zadané datové body