Speaker
Description
We have constructed new isobar models for photoproduction of various pseudoscalar mesons on nucleons [1,2] utilizing new experimental data from several collaborations. The higher spin nucleon (spin-3/2 and spin-5/2) and hyperon (spin-3/2) resonances were included using a consistent formalism and they were found to play an important role in the data description. In these analyses, we paid close attention to model predictions of the cross section at small kaon angles which are vital for accurate calculations of the hypernucleus-production cross section. In order to account for the unitarity corrections at the tree level, we introduced energy-dependent widths of nucleon resonances, which affect the choice of hadron form factors and the values of their cutoff parameters extracted in the fitting procedure.
Once all the ingredients of the model were well prepared, we faced the problem of selecting the appropriate set of resonances. Since a plain $\chi^2$ minimization, which we used in our previous studies [1,2], could not prevent us from overfitting the data, i.e. introducing more parameters (and thus resonances) than were needed for data description, we opted for methods known in machine learning. We used a regularization method called Least Absolute Shrinkage Selection Operator (LASSO) and information criteria for avoiding this issue and choosing the best fit. In the analysis of the then-new CLAS $K^+\Sigma^-$ data [3], we were then able to arrive at a very economical model including only the most needed resonances [4]. In our study of the role of hyperon resonances in the $K^+\Lambda$ channel, we made use of ridge regression to reduce some of the overly large couplings and arrive at much more robust model [5]. Subsequently, we focused on the photoproduction of $K^+\Sigma^0$ off protons and applied a model selection procedure with LASSO regularization in combination with criteria from information theory [6]. In our latest study focusing on the $\eta^\prime$ photoproduction, we used the Akaike Information Criterion together with Akaike differences and Akaike weights to select the most suitable model for data description [7,8].
[1] D. Skoupil, P. Bydžovský, Phys. Rev. C 93, 025204 (2016).
[2] D. Skoupil, P. Bydžovský, Phys. Rev. C 97, 025202 (2018).
[3] N. Zachariou et al., Phys. Lett. B 827, 136985 (2022).
[4] P. Bydžovský, A. Cieplý, D. Petrellis, D. Skoupil, and N. Zachariou, Phys. Rev. C 104, 065202 (2021).
[5] D. Petrellis, D. Skoupil, Phys. Rev. C 107, 045206 (2023).
[6] D. Petrellis, D. Skoupil, Phys. Rev. C 110, 065204 (2024).
[7] D. Skoupil, P. Bydžovský, D. Trnková, T. Akiyama, Phys. Rev. C 112, 025202 (2025).
[8] T. Akiyama et al., Prog. Theor. Exp. Phys. 2026, 043D04.