The potential of $\Lambda$ and $\Xi^-$ studies with PANDA at FAIR

TL;DR

This paper explores PANDA at FAIR's capabilities for hyperon studies, demonstrating high-efficiency reconstruction and precise spin measurements of hyperon-antihyperon pairs, advancing understanding of non-perturbative strong interactions and symmetries.

Contribution

It presents new simulation results showing PANDA's ability to reconstruct hyperon pairs with low background and to measure spin observables accurately using two efficiency correction methods.

Findings

High efficiency and low background in hyperon-antihyperon reconstruction

Successful measurement of hyperon polarization and spin correlations

Validation of efficiency correction methods for spin observable analysis

Abstract

The antiproton experiment PANDA at FAIR is designed to bring hadron physics to a new level in terms of scope, precision and accuracy. In this work, its unique capability for studies of hyperons is outlined. We discuss ground-state hyperons as diagnostic tools to study non-perturbative aspects of the strong interaction, and fundamental symmetries. New simulation studies have been carried out for two benchmark hyperon-antihyperon production channels: $\bar{p}p \to \bar{\Lambda}\Lambda$ and $\bar{p}p \to \bar{\Xi}^+\Xi^-$. The results, presented in detail in this paper, show that hyperon-antihyperon pairs from these reactions can be exclusively reconstructed with high efficiency and very low background contamination. In addition, the polarisation and spin correlations have been studied, exploiting the weak, self-analysing decay of hyperons and antihyperons. Two independent approaches to the finite efficiency have been applied and evaluated: one standard multidimensional efficiency correction approach, and one efficiency independent approach. The applicability of the latter was thoroughly evaluated for all channels, beam momenta and observables. The standard method yields good results in all cases, and shows that spin observables can be studied with high precision and accuracy already in the first phase of data taking with PANDA.