Determining the Neutrino Mass from Cyclotron Radiation Emission Spectroscopy (CRES)

克雷斯示范装置（CRESDA）将在今后两年中开发。学生将熟悉一系列主题，包括量子检测和放大，天线理论和信号处理。CRESDA将收集大量的时域天线波形，包括模拟的和真实的，这个博士项目的核心是开发技术来提取在热背景上几乎不可见的复杂信号。克雷斯信号可以具有各种形状，这取决于陷阱内的电子能量和运动。待探索的技术包括使用匹配滤波器将波形库与数据进行比较，以及提取最重要的特征，如回旋加速器发射的起始频率（携带有关电子衰变能量的信息）。光谱图的图像分析将用于识别我们数据中的电子轨迹。该项目的首要目标将是使用这些机器学习和数据分析技术来帮助预测未来采用克雷斯技术的中微子质量实验的最终范围。

The CRES Demonstration Apparatus (CRESDA) will be developed over the next two years. The student will become familiar with a range of topics including quantum detection & amplification, antenna theory and signal processing. CRESDA will collect a large volume of time-domain antenna waveforms, both simulated and real, and the heart of this PhD project is the development of techniques to extract complex signals that are barely visible above thermal background. The CRES signals can have a variety of shapes depending on the electron energy and motion inside the trap. Techniques to be explored include using matched-filters to compare a library of waveforms with the data, and the extraction of the most important features such as the starting frequency of the cyclotron emission (which carries information about the electron decay energy). Image analysis of spectrograms will be used to identify electron trajectories in our data. The over-arching aim of the project will be to use these machine-learning and data-analysis techniques to help inform projections for the ultimate reach of a future neutrino mass experiment employing the CRES technique.