Two-proton momentum correlation from photodisintegration of 
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-clustering light nuclei in the quasideuteron region

Huang, B.; Ma, Yugang

doi:10.1103/physrevc.101.034615

Cited by 23 publications

(18 citation statements)

References 86 publications

(112 reference statements)

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“…In the viewpoint of nuclear reaction, some observables have been proposed as probes of α clustering. For example, the giant dipole resonance spectrum [13][14][15], collective flows [16,17], and the nucleon emission from photoninduced reactions in quasi-deuteron region [18,19] were demonstrated as potential probes for α-clustering nuclei in low-intermediate energy domain. In relativistic heavyion collisions, it was suggested to provide experimental evidence of α clustering in light nuclei in their ground state through collisions against heavy nuclei [20,21].…”

Section: Introductionmentioning

confidence: 99%

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

He,

et al. 2021

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α-clustering structure is a significant topic in light nuclei. A Bayesian convolutional neural network (BCNN) is applied to classify initial non-clustered and clustered configurations, namely Woods-Saxon distribution and three-α triangular (four-α tetrahedral) structure for 12 C ( 16 O), from heavyion collision events generated within a multi-phase transport (AMPT) model. Azimuthal angle and transverse momentum distributions of charged pions are taken as inputs to train the classifier. On multiple-event basis, the overall classification accuracy can reach 95% for 12 C/ 16 O + 197 Au events at √ SNN = 200 GeV. With proper constructions of samples, the predicted deviations on mixed samples with different proportions of both configurations could be within 5%. In addition, setting a simple confidence threshold can further improve the predictions on the mixed dataset. Our results indicate promising and extensive possibilities of application of machine-learning-based techniques to real data and some other problems in physics of heavy-ion collisions.

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Section: Introductionmentioning

confidence: 99%

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

He,

et al. 2021

Preprint

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“…Specific spatial nucleon distribution incite us to search for different physical features between clustered nuclei and non-clustered cases. For example, our previous works demonstrated the effects of α-clustering structure on giant dipole resonance [9] and photonuclear reactions [13,14].…”

Section: Introductionmentioning

confidence: 99%

System dependence of away-side broadening and $α$-clustering light nuclei structure effect in dihadron azimuthal correlations

Wang,

Zhang,

2021

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We investigate the effect of α cluster in relativistic collisions via di-hadron azimuthal correlation. A collision system scan involving α-clustered 12 C and 16 O is studied by using a multiphase transport model in the most central collisions at √ sNN = 6.37 TeV. We compare the correlation functions of different configurations and calculate root-mean-square width and kurtosis in the away-side region. The momentum dependence of di-hadron azimuthal correlation is also presented. The results show substantial distinction in correlation functions between Woods-Saxon distribution and α-clustered structures. We argue that the difference related to jet quenching is originated from initial geometry anisotropy. This work proposed that di-hadron azimuthal correlation is a potential probe to distinguish α-clustered nuclei.

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“…Some probes were presented to explore sensitivity to the clustering structure. For instance, the collective observables show significant difference among various α-clustering structures in heavy-ion collisions [17][18][19][20][21], and nucleon-nucleon correlation displays different behavior in three-body photodisintegration of α-clustering nuclei [22][23][24][25]. In addition, another important probe, so-called giant dipole resonance (GDR) spectrum shows its sensitivity to different configurations of 12 C and 16 O in a framework of an extended quantum molecular dynamics (EQMD) model [26,27].…”

Section: Introductionmentioning

confidence: 99%

Dipole excitation of $^6$Li and $^9$Be studied with an extended quantum molecular dynamics model

Huang,

2021

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The α ( 4 He) -clustering structure is a common phenomenon in light nuclei due to the decreasing contribution of mean field in few-body system. In this work we presented calculations of giant dipole resonance (GDR) excitations for two non α-conjugate light nuclei, namely 6 Li and 9 Be, within a framework of an extended quantum molecular dynamics model. For 6 Li, we investigated the GDR spectra from the two-body clustering structure with α + deuteron as well as the threebody structure with α + n + p, and found that the major α-clustering contribution on the GDR peak is located at around 31 MeV, while the resonance contributions between clusters, namely α and deuteron or (n + p), are located on the lower energy side, which can be regarded as pygmy dipole resonance (PDR). For 9 Be, a mixture configuration contribution for the Chain-like structure and Borromean-like structure of the α + n + α configuration can somehow explain its GDR results.

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Two-proton momentum correlation from photodisintegration of α -clustering light nuclei in the quasideuteron region

Cited by 23 publications

References 86 publications

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

Machine-learning-based identification for initial clustering structure in relativistic heavy-ion collisions

System dependence of away-side broadening and $α$-clustering light nuclei structure effect in dihadron azimuthal correlations

Dipole excitation of $^6$Li and $^9$Be studied with an extended quantum molecular dynamics model

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