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Probing Initial State Clustering through Photon Anisotropic Flow in 7A TeV 16O+16O Collisions at the LHC

2025/09/25 by Sanchari Thakur, Pingal Dasgupta, Thakur, Sanchari +7
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Phenomenology (hep-ph) #High-Energy Particle Collisions Research #Nuclear Theory (nucl-th) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2509.20954

openalex publication_date 2025/09/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The presence of α clustered structures in light nuclei can enhance the initial spatial anisotropies in relativistic nuclear collisions relative to those arising from nuclei with uniform density distributions. Thus, observables that are strongly sensitive to the initial geometry can be a more efficient probe of the clustered structures than observables dominated by final state dynamics. We investigate the collisions of α clustered oxygen nuclei at √sNN=7A TeV at the LHC using the GLISSANDO initial state model along with the MUSIC event-by-event hydrodynamical framework. The tetrahedral α clustered structure of 16O leads to significantly larger initial triangular eccentricity ε3 than collisions with uniform density distributions especially in the most central events. The spatial eccentricity ε2 is found to be relatively less sensitive to the initial state clustered structure. The production of thermal photons is estimated to be only marginally influenced by clustering for both central as well as peripheral collisions. In contrast, the photon triangular flow coefficient v3(pT) is strongly affected by initial state clustering resulting in substantially larger values in both central and peripheral collisions. An experimental determination of photon anisotropic flow together with the ratios of flow coefficients in 16O+16O collisions therefore expected to provide valuable insight into the possible clustered structure in light nuclei and also to constrain parameters in theoretical modeling.

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