No Arabic abstract
Based on reflection symmetry in the reaction plane, it is shown that measuring the transverse spin-transfer coefficient $K_{yy}$ in the $bar{K}N to KXi$ reaction directly determines the parity of the produced cascade hyperon in a model-independent way as $pi_Xi =K_{yy}$, where $pi_Xi =pm 1$ is the parity. This result based on Bohrs theorem provides a completely general, universal relationship that applies to the entire hyperon spectrum. A similar expression is obtained for the photoreaction $gamma N to K K Xi$ by measuring both the double-polarization observable $K_{yy}$ and the photon-beam asymmetry $Sigma$. Regarding the feasibility of such experiments, it is pointed out that the self-analyzing property of the $Xi$s can be invoked, thus requiring only a polarized nucleon target.
Various model-independent aspects of the $bar{K} N to K Xi$ reaction are investigated, starting from the determination of the most general structure of the reaction amplitude for $Xi$ baryons with $J^P=frac12^pm$ and $frac32^pm$ and the observables that allow a complete determination of these amplitudes. Polarization observables are constructed in terms of spin-density matrix elements. Reflection symmetry about the reaction plane is exploited, in particular, to determine the parity of the produced $Xi$ in a model-independent way. In addition, extending the work of Biagi $mathrm{textit{et al. } [Z. Phys. C textbf{34}, 175 (1987)]}$, a way is presented of determining simultaneously the spin and parity of the ground state of $Xi$ baryon as well as those of the excited $Xi$ states.
The Migdal effect in a dark-matter-nucleus scattering extends the direct search experiments to the sub-GeV mass region through electron ionization with sub-keV detection thresholds. In this paper, we derive a rigorous and model-independent Migdal-photoabsorption relation that links the sub-keV Migdal process to photoabsorption. This relation is free of theoretical uncertainties as it only requires the photoabsorption cross section as the experimental input. Validity of this relation is explicitly checked in the case of xenon with an state-of-the-arts atomic calculation that is well-benchmarked by experiments. The predictions based on this relation for xenon, argon, semiconductor silicon and germanium detectors are presented and discussed.
The entanglement entropy of two-body elastic scattering at high energies is studied by using the model-independent Levy imaging method for investigating the hadron structure. It is considered the finite entropy in the momentum Hilbert space properly regularized and results are compared to recent evaluation using the diffraction peak approximation. We present the entropy for RHIC, Tevatron and LHC energies pointing out the underlying uncertainties.
The proton radius puzzle has motivated several new experiments that aim to extract the proton charge radius and resolve the puzzle. Recently PRad, a new electron-proton scattering experiment at Jefferson Lab, reported a proton charge radius of $0.831pm 0.007_textnormal{statistical}pm 0.012_textnormal{systematic}$. The value was obtained by using a rational function model for the proton electric form factor. We perform a model-independent extraction using $z$-expansion of the proton charge radius from PRad data. We find that the model-independent statistical error is more than 50% larger compared to the statistical error reported by PRad.
We develop a robust method to extract the pole configuration of a given partial-wave amplitude. In our approach, a deep neural network is constructed where the statistical errors of the experimental data are taken into account. The teaching dataset is constructed using a generic S-matrix parametrization, ensuring that all the poles produced are independent of each other. The inclusion of statistical error results into a noisy classification dataset which we should solve using the curriculum method. As an application, we use the elastic $pi N$ amplitude in the $I(J^P)=1/2(1/2^{-})$ sector where $10^6$ amplitudes are produced by combining points in each error bar of the experimental data. We fed the amplitudes to the trained deep neural network and find that the enhancements in the $pi N$ amplitude are caused by one pole in each nearby unphysical sheet and at most two poles in the distant sheet. Finally, we show that the extracted pole configurations are independent of the way points in each error bar are drawn and combined, demonstrating the statistical robustness of our method.