论文标题

通过带电的电流散射到激发状态的液体氙气检测器中的太阳中微子检测

Solar neutrino detection in liquid xenon detectors via charged-current scattering to excited states

论文作者

Haselschwardt, Scott, Lenardo, Brian, Pirinen, Pekka, Suhonen, Jouni

论文摘要

我们通过液体氙时间投影室中带电的中微子核散射过程实时检测太阳中微子的实时检测前景。我们使用具有实验数据的基准测试的核壳模型来计算中微子捕获在$^{131} $ XE和$^{136} $ xe上产生的剖腹核的特定激发态的横截面。外壳模型进一步用于计算$^{136} $ cs的低洼$ 1^{+} $兴奋状态的衰减方案,其中有稀疏的实验数据。我们探索使用两种技术使用事件拓扑结构的空间分离能量沉积和使用延迟的巧合使用时间分离,探讨了使用两种技术标记特征性去激发$γ$ -rays/转换电子的可能性。在每种情况下的效率均在一系列逼真的检测参数范围内评估。我们发现,拓扑特征可能以ra的背景为主,但是从$^{136} $ cs中预测的长期延迟的一致性签名可能会在下一代实验中对CNO Neutrino相互作用进行无背景的检测,而不是当前的测量值较小。我们还估计灵敏度是暴露的函数,用于检测$^{7} $中的太阳温度诱导的线变位为中微子发射,这可能会提供新的太阳能模型测试。

We investigate the prospects for real-time detection of solar neutrinos via the charged-current neutrino-nucleus scattering process in liquid xenon time projection chambers. We use a nuclear shell model, benchmarked with experimental data, to calculate the cross sections for populating specific excited states of the caesium nuclei produced by neutrino capture on $^{131}$Xe and $^{136}$Xe. The shell model is further used to compute the decay schemes of the low-lying $1^{+}$ excited states of $^{136}$Cs, for which there is sparse experimental data. We explore the possibility of tagging the characteristic de-excitation $γ$-rays/conversion electrons using two techniques: spatial separation of their energy deposits using event topology and their time separation using delayed coincidence. The efficiencies in each case are evaluated within a range of realistic detector parameters. We find that the topological signatures are likely to be dominated by radon backgrounds, but that a delayed coincidence signature from long-lived states predicted in $^{136}$Cs may enable background-free detection of CNO neutrino interactions in next-generation experiments with smaller uncertainty than current measurements. We also estimate the sensitivity as a function of exposure for detecting the solar-temperature-induced line shift in $^{7}$Be neutrino emission, which may provide a new test of solar models.

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