Extraction of the n structure function F2n from inclusive scattering data on composite nuclei
A. S. Rinat, M. F. Taragin
Abstract
We consider a generalized convolution, linking Structure Functions (SF) FN2 for nucleons, FA2 for a physical nucleus and fPN,A for a nucleus, composed of point-nucleons. In order to extract F2n we employ data on F2p,A and the computed fPN,A. Only for Q2≈ 3.5 GeV2 do data permit the extraction of F2A(x,3.5) over a sufficiently wide x-range. Applying Mellin transforms, the above relation between SF turns into an algebraic one, which one solves for the Mellin transform of the unknown F2n. We present inversion methods leading to the desired F2n, all using a parametrization for C(x,Q2)=F2n(x,Q2)/F2p(x,Q2). Imposing motivated constraints, the simplest parametrization leaves one free parameter C(x=1,Q2). For Q2= 3.5 GeV2 its average over several targets and different methods is <C(1,3.5)>=0.540.03. We argue that for the investigated Q2, C(x 1,3.5) is determined by the nucleon-elastic (NE) part of SF. The calculated value is near the extracted one and both are close to the SU(6) limit 2/3.
Create a lesson
Related papers
Spurious center-of-mass mode as an S-matrix pole within the Jost-random-phase approximation
Kazuhito Mizuyama
Quantum computing for high energy nuclear physics
João Barata
Elliptic and triangular flow responses in O+O and Ne+Ne collisions from hydrodynamics and kinetic theory
Victor E. Ambrus, Giuliano Giacalone, Clemens Werthmann et al.
Information content of the Gamow Teller Resonance: Bayesian inference with self-consistent Skyrme models
Zidu Lin, Gianluca Colò, Andrew W. Steiner et al.
Pionization experiments in light nuclei
Slawomir Wycech, Guillaume Hupin, Jaume Carbonell
Deep Learning for Longitudinal Asymmetry in Heavy-Ion Collisions at RHIC Energies
Anup Kumar Sikdar, Sanchari Thakur, Pingal Dasgupta et al.