2007
DOI: 10.1016/j.physletb.2007.09.036
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Electroproduction of kaons from the proton in a Regge-plus-resonance approach

Abstract: We present a Regge-plus-resonance (RPR) description of the p(e, e ′ K + )Y processes (Y = Λ, Σ 0 ) in the resonance region. The background contributions to the RPR amplitude are constrained by the high-energy p(γ, K + )Y data. As a result, the number of free model parameters in the resonance region is considerably reduced compared to typical effective-Lagrangian approaches. We compare a selection of RPR model variants, originally constructed to describe KY photoproduction, with the world electroproduction data… Show more

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Cited by 50 publications
(75 citation statements)
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(88 reference statements)
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“…Several groups have stressed that the importance of the background contributions calls for a framework that accounts for both the resonant and non-resonant processes and that provides for a means to constrain both of these classes of reaction mechanisms independently [27,28].…”
Section: Theoretical Modelsmentioning
confidence: 99%
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“…Several groups have stressed that the importance of the background contributions calls for a framework that accounts for both the resonant and non-resonant processes and that provides for a means to constrain both of these classes of reaction mechanisms independently [27,28].…”
Section: Theoretical Modelsmentioning
confidence: 99%
“…The electroproduction observables have been shown to yield important complementary insights [27]. Some of the most important aspects of electroproduction include:…”
Section: Theoretical Modelsmentioning
confidence: 99%
See 2 more Smart Citations
“…This is due, in part, to the difficulties encountered in modeling the strong interaction in the non-perturbative regime. As such, the problem has been approached through the use of effective field theories [1][2][3][4], Regge models [5] and hybrid Regge-plus-resonance (RPR) models [6,7], and more recently, through coupled-channel analyses [8][9][10][11]. All of these methods require large and precise data sets in order to constrain fitting parameters.…”
Section: Introductionmentioning
confidence: 99%