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Matches in UGent Biblio for { ?s ?p A long-standing goal of hadronic physics is to obtain a detailed map of the nucleon's resonance spectrum. This would help bridge the gap between hadrodynamical models on the one hand and constituent quark models on the other hand. Some quark models predict so-called "missing resonances" that have not been found in analyses of pion-nucleon scattering data. Input from non-pionic reactions such as open-strangeness photoproduction is key to resolve the status of these missing resonances, as some are predicted to couple more strongly to non-pionic channels. Despite the increasing availability of experimental data for p(gamma,K^+)Lambda, different hadrodynamical models reach contradictory conclusions with respect to the contributing resonances. The cause of this disparity is twofold. First, the important role played by the non-resonant dynamics makes the precise parametrisation of the background amplitude a great source of model dependence. Second, the criterion to determine whether a resonance contributes significantly or insignificantly varies among different analyses. This work aims to address these two issues so as to unambiguously determine the resonance content of p(gamma,K^+)Lambda. The reaction p(gamma,K^+)Lambda is described by the Regge-plus-resonance (RPR) model. The RPR model combines Regge theory and elements from the isobar approach into an economical model for kaon photo- and electroproduction in and above the resonance region. In the RPR approach, the parameters of the reggeised background are constrained to high-energy data. This approach allows for a clear separation between the non-resonant background and the s-channel resonance contributions. The second issue boils down to the question: "How probable is resonance R, given experimental data?" Adding resonances improves a model's description of the data, but does not necessarily represent the correct dynamics. Bayesian inference provides a statistically solid way to do model selection, that naturally includes the principle of Occam's razor. The conditional probability of a model given data is quantified by its Bayesian evidence Z. This dissertation presents a method to compute the evidence and to evaluate the probabilities of a proposed set of resonances. Using this method, we find that the resonances S11(1535), S11(1650), F15(1680), P13(1720), D13(1900), P13(1900), P11(1900), and F15(2000) have the highest probability of contributing to p(gamma,K^+)Lambda.. }

• aggregation abstract "A long-standing goal of hadronic physics is to obtain a detailed map of the nucleon's resonance spectrum. This would help bridge the gap between hadrodynamical models on the one hand and constituent quark models on the other hand. Some quark models predict so-called "missing resonances" that have not been found in analyses of pion-nucleon scattering data. Input from non-pionic reactions such as open-strangeness photoproduction is key to resolve the status of these missing resonances, as some are predicted to couple more strongly to non-pionic channels. Despite the increasing availability of experimental data for p(gamma,K^+)Lambda, different hadrodynamical models reach contradictory conclusions with respect to the contributing resonances. The cause of this disparity is twofold. First, the important role played by the non-resonant dynamics makes the precise parametrisation of the background amplitude a great source of model dependence. Second, the criterion to determine whether a resonance contributes significantly or insignificantly varies among different analyses. This work aims to address these two issues so as to unambiguously determine the resonance content of p(gamma,K^+)Lambda. The reaction p(gamma,K^+)Lambda is described by the Regge-plus-resonance (RPR) model. The RPR model combines Regge theory and elements from the isobar approach into an economical model for kaon photo- and electroproduction in and above the resonance region. In the RPR approach, the parameters of the reggeised background are constrained to high-energy data. This approach allows for a clear separation between the non-resonant background and the s-channel resonance contributions. The second issue boils down to the question: "How probable is resonance R, given experimental data?" Adding resonances improves a model's description of the data, but does not necessarily represent the correct dynamics. Bayesian inference provides a statistically solid way to do model selection, that naturally includes the principle of Occam's razor. The conditional probability of a model given data is quantified by its Bayesian evidence Z. This dissertation presents a method to compute the evidence and to evaluate the probabilities of a proposed set of resonances. Using this method, we find that the resonances S11(1535), S11(1650), F15(1680), P13(1720), D13(1900), P13(1900), P11(1900), and F15(2000) have the highest probability of contributing to p(gamma,K^+)Lambda.".