Title: Identifying environmentally induced calibration changes in cryogenic RF axion detector systems using Deep Neural Networks Show Chinese title

Title: 使用深度神经网络识别低温射频轴子探测器系统中的环境诱导校准变化

Abstract: The axion is a compelling hypothetical particle that could account for the dark matter in our universe, while simultaneously explaining why quark interactions within the neutron do not appear to give rise to an electric dipole moment. The most sensitive axion detection technique in the 1 to 10 GHz frequency range makes use of the axion-photon coupling and is called the axion haloscope. Within a high Q cavity immersed in a strong magnetic field, axions are converted to microwave photons. As searches scan up in axion mass, towards the parameter space favored by theoretical predictions, individual cavity sizes decrease in order to achieve higher frequencies. This shrinking cavity volume translates directly to a loss in signal-to-noise, motivating the plan to replace individual cavity detectors with arrays of cavities. When the transition from one to (N) multiple cavities occurs, haloscope searches are anticipated to become much more complicated to operate: requiring N times as many measurements but also the new requirement that N detectors function in lock step. To offset this anticipated increase in detector complexity, we aim to develop new tools for diagnosing low temperature RF experiments using neural networks for pattern recognition. Current haloscope experiments monitor the scattering parameters of their RF receiver for periodically measuring cavity quality factor and coupling. However off-resonant data remains relatively useless. In this paper, we ask whether the off resonant information contained in these VNA scans could be used to diagnose equipment failures/anomalies and measure physical conditions (e.g., temperatures and ambient magnetic field strengths). We demonstrate a proof-of-concept that AI techniques can help manage the overall complexity of an axion haloscope search for operators. Show Chinese abstract

Abstract: 轴子是一种引人注目的假设性粒子，它可能解释我们宇宙中的暗物质，并同时解释为什么中子内部的夸克相互作用似乎不会产生电偶极矩。 在 1 到 10 GHz 频率范围内，最灵敏的轴子探测技术利用了轴子-光子耦合，称为轴子腔检测器。 在一个强磁场中浸没的高 Q 腔内，轴子被转换为微波光子。 随着搜索范围向理论预测所偏好的参数空间扩展，轴子质量增加时，单个腔体的尺寸减小以达到更高的频率。 这种缩小的腔体体积直接导致信噪比下降，因此计划用腔体阵列取代单个腔体探测器。 当从单腔过渡到多个腔体（N）时，腔检测器的搜索预计会变得更加复杂：需要进行 N 倍的测量，同时也需要新的要求，即 N 个探测器必须同步运行。 为了抵消这种预期的探测器复杂度增加，我们旨在开发新的工具，使用神经网络来进行低温射频实验的故障诊断。 目前的腔检测器实验通过监测射频接收器的散射参数来周期性地测量腔的质量因子和耦合。 然而，非共振数据相对无用。 在这篇论文中，我们探讨这些 VNA 扫描中包含的非共振信息是否可以用于诊断设备故障/异常以及测量物理条件（例如温度和环境磁场强度）。 我们证明了人工智能技术可以帮助操作员管理轴子腔检测器搜索的整体复杂性。