JOURNAL ARTICLE
Studies of angular distribution of the grey particles in 16O-emulsion collision at 3.7A GeV.
Published In: International Journal of Modern Physics E: Nuclear Physics, 2023, v. 32, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Abdallah, N. 3 of 3
Abstract
By means of 4 π emulsion track detectors, we report the characteristics of the experimental angular distributions of the fast target protons (grey particles) emitted in interactions of 1 6 O with emulsion nuclei at 3.7A GeV. Grey tracks in nuclear emulsion corresponding protons with kinetic energy in the ranges 2 6 < E K. E ≤ 4 0 0 MeV. The angular distributions are compared with those resulting from interactions of other projectiles ( 1 2 C, 2 2 Ne and 2 8 Si) at nearly the same incident energy. These angular distributions are studied at different target sizes ( N h -values) and at various projectile spectator charges (Q -values) in the framework of the modified Maxwell– Boltzmann statistical model. The nuclear limiting fragmentation hypothesis is shown to be fulfilled for these target protons angular distributions which are found to be independent of the target size and centrality of collisions. Also, the size of the projectile is found to be ineffective at Dubna energies. The angular distributions of all groups of events possess peaks positioned at θ g ∼ 6 0 ∘ . The calculated statistical moments of the distributions along with skewness and kurtosis are investigated in addition to the forward-backward asymmetry ratio (F ∕ B) for the different groups of events. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modern Physics E: Nuclear Physics. 2023/02, Vol. 32, Issue 2, p1
- Document Type:Article
- Subject Area:Law
- Publication Date:2023
- ISSN:0218-3013
- DOI:10.1142/S0218301323500064
- Accession Number:163408788
- Copyright Statement:Copyright of International Journal of Modern Physics E: Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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