Statistical methods for Direct Dark Matter Searches with LZ

LZ 直接暗物质搜索的统计方法

• 批准号: 2587450
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2587450
• 关键词: Statistical; methods; Direct; Dark; Matter

Profile likelihood ratio (PLR) analyses are utilized in many physics searches to quantify the certainty of an observation, or set a limit. Over the lifetime of an experiment, the software to complete such searches evolves, and needs be both maintained and operated for the successful completion of physics analyses. In particular, searches that look at effective field theory operators usually must run this limit code 28 times, compared to the 2 times for the basic spin- independent and dependent cases. Thus the efficiency of such code is of greater import in such studies. This student will assist in the maintenance, evaluation, and use of the profile likelihood analysis code in LZ, including options in both ROOT?C+ and python. The LZ detector intends to be the most well understood dark matter detector built to date, as it continues in a technology that has evolved in multiple iterations. This allows the limit setting code to be increasingly specific in what nuisance parameters are used, what their uncertainties are, and how detector parameters evolve over the lifetime of the experiment. Incorporating such levels of detail into the PLR, while still allowing for reasonable computation time, will be the primary activity of this project. The student will be trained during initial science runs of the experiment, and will take a leading role in running the PLR code on later science runs.The student will have some flexibility in choosing other analysis tasks dependent on their interests and the collaboration needs. As background and signal models, calibrations, and detector livetime all feed directly into the PLR operation, there are tight logical ties to almost any task a student self selects. With the focus on effective field theory models, where the search is extended to higher energy nuclear recoils, there are new backgrounds (multi-scatter single-ionization events) and detector effects (PMT saturation) that must be taken into account, and new attention to data quality cuts. For instance, and energy dependent fiducial cut to remove backgrounds from the walls of the detector will need to be established to maximize sensitivity beyond the standard SI WIMP search.The student will also gain hardware experience with detector R&D for the next generation of direct dark matter experiments, working on tasks as needed within Oxford's expertise in sensors, electronics, high voltage, as well as readout, cabling, radiopurity and cleanliness. This will include vacuum and gas system handling, and with the larger LZ collaboration, cryogenics and photosensors.

轮廓似然比 (PLR) 分析在许多物理搜索中用于量化观察的确定性或设置限制。在实验的整个生命周期中，完成此类搜索的软件不断发展，并且需要维护和操作才能成功完成物理分析。特别是，寻找有效场论算子的搜索通常必须运行此极限代码 28 次，而基本的自旋无关和相关情况则需要运行 2 次。因此，此类代码的效率在此类研究中更为重要。该学生将协助LZ中的轮廓似然分析代码的维护、评估和使用，包括ROOT？C+和python中的选项。 LZ 探测器旨在成为迄今为止最容易理解的暗物质探测器，因为它延续了经过多次迭代发展的技术。这使得限制设置代码在使用哪些干扰参数、它们的不确定性是什么以及探测器参数在实验的整个生命周期中如何演变方面变得越来越具体。将此类详细程度纳入 PLR，同时仍允许合理的计算时间，将是该项目的主要活动。学生将在实验的初始科学运行期间接受培训，并将在以后的科学运行中运行 PLR 代码中发挥主导作用。学生将根据自己的兴趣和协作需求灵活选择其他分析任务。由于背景和信号模型、校准和探测器生存时间都直接输入到 PLR 操作中，因此与学生自己选择的几乎所有任务都存在紧密的逻辑联系。随着对有效场论模型的关注，搜索扩展到更高能量的核反冲，必须考虑新的背景（多散射单电离事件）和探测器效应（PMT 饱和），以及对数据质量削减的新关注。例如，需要建立能量依赖基准切割来去除探测器壁上的背景，以最大限度地提高超出标准 SI WIMP 搜索的灵敏度。学生还将获得下一代直接暗物质实验探测器研发的硬件经验，根据牛津大学在传感器、电子、高压以及读出、布线、放射性纯度和清洁度方面的专业知识所需的任务进行工作。这将包括真空和气体系统处理，以及与更大的 LZ 的合作、低温技术和光电传感器。