标题： 利用Q-Pix像素读出技术研究太阳中微子的机会与挑战 显示英文标题

标题： Opportunities and challenges to study solar neutrinos with a Q-Pix pixel readout

摘要： 太阳中微子的研究在天体物理学、核物理学和粒子物理学中提供了重要的机遇。 然而，这些中微子的低能特性给它们与背景事件的分离带来了相当大的挑战，需要具有低能阈值、高空间和能量分辨率以及低数据率的探测器。 我们介绍了使用地下千吨级液态氩探测器进行太阳中微子研究的情况，并采用了基于Q-Pix技术的像素读出方式。 我们探讨了利用体积限域化、方向拓扑信息、光信号符合和脉冲形状鉴别来增强太阳中微子灵敏度的潜力。 我们发现，区分低于5 MeV的中微子信号非常困难。 然而，我们证明当外部背景足够了解且探测器使用低背景技术建造时，这些方法对于太阳中微子的探测是有用的。 在为本研究构建可行的背景模型时，我们确定来自洞室墙壁的{\gamma }背景以及来自氡衰变链中{\alpha }粒子俘获的背景对于太阳中微子灵敏度至关重要，并且现有测量无法充分约束这些背景。 最后，我们强调了在太阳中微子研究中使用Q-Pix的主要优势在于其能够以最小的数据率连续读取所有低能事件，并为后续离线分析提供可管理的存储。 显示英文摘要

摘要： The study of solar neutrinos presents significant opportunities in astrophysics, nuclear physics, and particle physics. However, the low-energy nature of these neutrinos introduces considerable challenges to isolate them from background events, requiring detectors with low-energy threshold, high spatial and energy resolutions, and low data rate. We present the study of solar neutrinos with a kiloton-scale liquid argon detector located underground, instrumented with a pixel readout using the Q-Pix technology. We explore the potential of using volume fiducialization, directional topological information, light signal coincidence and pulse-shape discrimination to enhance solar neutrino sensitivity. We find that discriminating neutrino signals below 5 MeV is very difficult. However, we show that these methods are useful for the detection of solar neutrinos when external backgrounds are sufficiently understood and when the detector is built using low-background techniques. When building a workable background model for this study, we identify {\gamma} background from the cavern walls and from capture of {\alpha} particles in radon decay chains as both critical to solar neutrino sensitivity and significantly underconstrained by existing measurements. Finally, we highlight that the main advantage of the use of Q-Pix for solar neutrino studies lies in its ability to enable the continuous readout of all low-energy events with minimal data rates and manageable storage for further offline analyses.