Date of Award

Summer 2023

Document Type

Open Access Thesis


Physics and Astronomy

First Advisor

Yordanka Ilieva


Improving and adding new experimental measurements of hyperon-nucleon (YN) and hyperon-deuteron (Yd) cross sections is an active area in nuclear physics research. Scientists use such cross-section data to understand the composition of neutron stars and develop a comprehensive picture of the baryon-baryon interaction. Using high luminosity photon beam incident on a long liquid deuterium target and the CEBAF Large Acceptance Spectrometer (CLAS) at the Thomas Jefferson National Accelerator Facility (JLab), a sample of hyperon-deuteron elastic scattering events was previously identified. The E06-103 (g13) experiment was originally designed to address the scarcity of experimental data on hyperon photoproduction off the neutron in support of the JLab N* program. However, the high statistics allows us to obtain meaningful signal for hyperon-deuteron scattering, where the hyperon beam is first photoproduced on a deuteron nucleus and then scattered on another nucleus in the same target cell. In order to obtain cross-section estimates from the elastic hyperon-deuteron sample, the number of beam lambda hyperons needs to be known. In this work, we extract the yield in γd → ΛX reaction using data taken in the g13a experiment. By detecting the final state proton and π − pairs, the Λ hyperons are identified. Background subtraction performed on event distributions over the hyperon mass yields the number of ΛX events originating within the target for various hyperon momenta and polar angles. A correction factor to the yields accounting for the error due to our assumption that the hyperon mass has a Gaussian shape has been estimated to be 15.5%. This analysis has established the foundational methods and techniques needed for the determination of the inclusive Λ photoproduction yields in the g13 experiment and has provided preliminary yield estimates. Future work will include estimates of systematic uncertainties and acceptance normalization. This work was supported in part by the U.S. NSF under award 2111050.


© 2023, K H P Nishadi Hasarangi Silva

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