https://doi.org/10.1140/epjc/s10052-007-0381-9
Regular Article - Theoretical Physics
Longitudinal polarisation of Λ and Λ̄ hyperons in lepton–nucleon deep-inelastic scattering
1
Theory Division, Physics Department, CERN, 1211, Geneva, 23, Switzerland
2
JINR, Dubna, Russia
3
INFN, Torino, Italy
4
Yerevan Physics Inst., Yerevan, Armenia
5
INFN, Florence, Italy
* e-mail: naumov@nusun.jinr.ru
Received:
12
March
2007
Revised:
11
June
2007
Published online:
15
August
2007
We consider models for the spin transfers to Λ and Λ̄ hyperons produced in lepton–nucleon deep-inelastic scattering. We make predictions for longitudinal Λ and Λ̄ spin transfers for the COMPASS experiment and for HERA, and for the spin transfer to Λ hyperons produced at JLAB. We demonstrate that accurate measurements of the spin transfers to Λ and Λ̄ hyperons with COMPASS kinematics have the potential to probe the intrinsic strangeness in the nucleon. We show that a measurement of Λ̄ polarisation could provide a clean probe of the spin transfer from s̄ quarks and provides a new possibility to measure the antistrange quark distribution function. COMPASS data in a domain of x that has not been studied previously will provide valuable extra information to fix models for the nucleon spin structure. The spin transfer to Λ̄ hyperons, which could be measured by the COMPASS experiment, would provide a new tool to distinguish between the SU(6) and Burkardt–Jaffe (BJ) models for baryon spin structure. In the case of the HERA electron–proton collider experiments with longitudinally-polarised electrons, the separation between the target and current fragmentation mechanisms is more clear. It provides a complementary probe of the strange quark distribution and helps distinguish between the SU(6) and BJ models for the Λ and Λ̄ spin structure. Finally, we show that the spin transfer to Λ hyperons measured in a JLAB experiment would be dominated by the spin transfer of the intrinsic polarised-strangeness in the remnant nucleon, providing an independent way to check our model predictions.
© Springer-Verlag , 2007
