Data Quality for the LUX-ZEPLIN detector, and HV performance R&D

LZ直接暗物质探测器的数据质量研究分为两大类：需要从分析中删除某些时间段的问题，以及需要从分析中删除特定事件的问题。该学生将在第一次LZ科学运行中参与研究数据质量，并在随后的科学运行中担任数据质量评估和削减的领导角色。这项分析任务的目的是通过适当排除非暗物质背景并在数据集中包括潜在的信号事件来最大化LZ对暗物质信号的敏感性。对寻找可能泄漏到信号区的新的背景事件群的机器学习技术进行评估，以查看它们是否节省了人类分析员的时间和精力，并且比人类分析员更有效。这样的削减需要研究事件群，这些事件群通常远离我们的分析区域，并且通常通过能量或位置重建而被削减，但可能包含比在受限搜索数据集中可以看到的更多的数据质量问题的例子。LUX、ZEPLIN和LZ分析团队尚未在数据质量研究中使用机器学习，其他团队主要在寻找波形异常，或受限数据集中的异常，而不是更大的数据集中的异常。需要对所有新探测器和探测器的每个新数据采集周期进行数据质量研究。执行这些分析切割通常是科学研究中最耗费资源的部分。探测器的高电压性能是导致数据质量问题的重要因素，高压组件的额外电子发射是主要问题。学生将开始一项设计、工程和测试项目，为未来时间投影室的高压电极网做准备。一项新的实验将需要可以就地组装的模块网格，并将通过光收集、工程和电场模拟来设计此类电极，使其具有最大限度的光收集和电场均匀。将生产和测试各种规模的工程样机。此外，还将操作小规模测试电极，以建立关于初始电网运行和烧毁/调节的统计数据，以了解大型电极电网的最佳调试计划是什么。可以研究多电极技术，从实验者编织的导线、商业网状物、蚀刻网状物和薄膜沉积。

Data quality studies in the LZ direct dark matter detector fall into two broad categories: issues that require eliminating certain time periods from analysis, and issues that require cutting particular events from analysis. This student will participate in studying data quality in the first LZ science run, and take a leading role in data quality assessment and cuts in subsequent science runs. The aim of this analysis task is to maximize LZ sensitivity to dark matter signals with the appropriate exclusion of non-dark matter backgrounds and inclusion of potential signal events in the dataset. Machine learning techniques looking for new background event populations that may leak into the signal region be evaluated to see if they save time and effort of human analysts, and are more effective than human analysts. Such cuts require studying event populations that generally fall far from our analysis region and are usually cut by energy or position reconstruction, but may contain many more examples of data quality problems than can be seen in the restricted search dataset. the LUX, ZEPLIN, and LZ analysis teams have not yet utilized machine learning in data quality studies, and other groups are primarily looking for waveform anomalies, or anomalies within the restricted dataset, not within the greater dataset. Data quality studies are required for all new detectors, and every new data taking period of the detector. Performing these analysis cuts is often the most resource intensive part of the science searches.The high voltage performance of the detector is a significant contributor to data quality issues with additional electron emission from HV components being the primary concern. The student will embark on a design, engineering, and testing project to prepare for high voltage electrode grids for future time projection chambers. A new experiment will require modular grids that can be assembled in situ, and engineering such electrodes with the maximization of light collection and electric field uniformity will be performed with light collection, engineering, and electric field simulations. Engineering prototypes of varies scales will be produced and tested. In addition, small scale test electrodes will be operated to build statistics on initial grid operation and burn in/conditioning to understand what the optimal commissioning plan is for a large scale electrode grid. multiple electrode technologies may be studied, from wires woven by experimentalists, commercial meshes, etched meshes, and thin film depositions.