arXiv subjects

R. Fang

Publications and source records attributed to R. Fang.

4 recordsLinked to original sources

arXiv2024

Strength measurement of the $E_{\alpha}^{lab}$ = 830 keV resonance in $^{22}\rm{Ne}(\alpha,n)^{25}\rm{Mg}$ reaction using a stilbene detector

The interplay between the $^{22}$Ne$(\alpha,\gamma)^{26}$Mg and the competing $^{22}$Ne$(\alpha,n)^{25}$Mg reactions determines the efficiency of the latter as a neutron source at the temperatures of stellar helium burning. In both cases, the rates are dominated by the $\alpha$-cluster resonance at 830 keV. This resonance plays a particularly important role in determining the strength of the neutron flux for both the weak and main $s$-process as well as the $n$-process. Recent experimental studies based on transfer reactions suggest that the neutron and $\gamma$-ray strengths for this resonance are approximately equal. In this study, the $^{22}$Ne$(\alpha,n)^{25}$Mg resonance strength has been remeasured and found to be similar to the previous direct studies. This reinforces an 830 keV resonance strength that is approximately a factor of three larger for the $^{22}$Ne$(\alpha,n)^{25}$Mg reaction than for the $^{22}$Ne$(\alpha,\gamma)^{26}$Mg reaction.

nucl-ex↗

arXiv2024

Energy, strength, and alpha width measurements of $E_{\rm{c.m.}} = 1323$ and $1487$ keV resonances in $^{15}$N($\alpha,\gamma$)$^{19}$F

The $^{15}$N($\alpha,\gamma$)$^{19}$F reaction produces $^{19}$F in asymptotic giant branch (AGB) stars, where the low energy tails of two resonances at $E_{\rm{c.m.}} = 1323 \pm 2$ and $1487 \pm 1.7$ keV are estimated to contribute about $30\%$ of the total reaction rate in these environments. However, recent measurements have shown discrepancies in the energies, the strengths, and the corresponding alpha widths of these two resonances, resulting in an increase in the systematic uncertainty of the extrapolated cross section to helium burning energies. With this motivation, we have undertaken new measurements of the $^{15}$N$(\alpha,\gamma)^{19}$F at the University of Notre Dame Nuclear Science Laboratory. The setup consisted of an alpha particle beam impinged on a solid Ti$^{15}$N target with gamma-ray spectroscopy accomplished using a high purity germanium detector. Using the Doppler corrected gamma-ray energies, we confirmed the lower resonance energy to be $1321.6 \pm 0.6$ keV and found a value for the higher one of $1479.4 \pm 0.6$ keV that is more consistent with those found from previous elastic scattering studies. We found that the resonance strengths for both were consistent with most values found in the literature, but a larger alpha width has been recommended for the $E_{\rm{c.m.}} = 1487$ keV resonance. The larger alpha width suggests a reaction rate increase of about $15\%$ at temperatures $T < 0.1$ GK relevant to low mass AGB stars. The impact of the increased reaction rate requires further investigations.

nucl-ex↗

arXiv2021

FACET: A new long-lived particle detector in the very forward region of the CMS experiment

We describe a proposal to add a set of very forward detectors to the CMS experiment for the high-luminosity era of the Large Hadron Collider to search for beyond the standard model long-lived particles, such as dark photons, heavy neutral leptons, axion-like particles, and dark Higgs bosons. The proposed subsystem is called FACET for Forward-Aperture CMS ExTension, and will be sensitive to any particles that can penetrate at least 50 m of magnetized iron and decay in an 18 m long, 1 m diameter vacuum pipe. The decay products will be measured in detectors using identical technology to the planned CMS Phase-2 upgrade.

hep-ex↗

arXiv2020

Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks

Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising.

physics.flu-dyn↗