Abstract

Cross-section distributions are calculated for the reaction e+e−→J/ψ→Λ¯(→p¯π+)Λ(→pπ−), and related annihilation reactions mediated by vector mesons. The hyperon-decay distributions depend on a number of structure functions that are bilinear in the, possibly complex, psionic form factors GMψ and GEψ of the Lambda hyperon. The relative size and relative phase of these form factors can be uniquely determined from the unpolarized joint-decay distributions of the Lambda and anti-Lambda hyperons. Also the decay-asymmetry parameters of Lambda and anti-Lambda hyperons can be determined.

Highlights

  • Two hadronic form factors, commonly called G M (s) and G E (s), areneeded for the description of, Fig. 1a, and by varying the the annihilati√on process e−e+ → c.m. energy s, their numerical values can in principle be determined for all s values abovethreshold

  • G M (s) and G E (s) in the time-like region was reported in Ref. [4]

  • The polarization distributions Hab are each expressed in terms of structure functions that depend on the scattering angle θ, the ratio function α(s), and the phase function (s)

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Summary

Introduction

Two hadronic form factors, commonly called G M (s) and G E (s), areneeded for the description of , Fig. 1a, and by varying the the annihilati√on process e−e+ → c.m. energy s, their numerical values can in principle be determined for all s values abovethreshold. For the general case of annihilation via an intermediate photon, the joint (→ pπ −) ̄ (→ pπ +) decay distributions were calculated and analyzed in Ref. The interesting special case of annihilation through an intermediate J /ψ or ψ(2S), Fig. 1b, has been investigated in several theoretical [5,6] and experimental papers [7,8,9] This process has been used for determination of the anti-Lambda decay-asymmetry parameter and for CP symmetry tests in the hyperon system. In order to specify events and compare measured data with theoretical predictions, we need distribution functions expressed in some specific coordinate system.

Basic necessities
Folding of distributions
Differential distributions

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