He using machine learning and other algorithms*

We are investigating the use of quartz tuning forks in liquid ³He as small sample thermometers at ultra-low temperatures near 1 mK and in high magnetic fields up to 16 T. The resonant frequencies and damping are dependent on the viscosity of the surrounding ³He, which has a well-known temperature dependence [1]. Such thermometry has been successfully used in relatively large ³He cells. Its adaptation to thermal studies of small solid samples requires engineering miniaturized ³He cells with thermal masses comparable to the samples and improving data acquisition and analysis techniques [2]. The traditional measurement setup uses a frequency sweep to attain the resonant curve which is used to generate one temperature data point. The time required for full frequency sweep of 100 data points is on the order of two minutes, compared to estimated thermalization times around 30 seconds. Using optimized analysis techniques and measurement methods guided by machine learning, we aim to improve both the thermal resolution and data acquisition times of our thermometers. We use machine learning as a powerful tool to select maximally useful frequency point measurements for resonance sweeps while maintaining the accuracy of the curve features. We investigate other optimizations such as resonance tracking to further improve resolution and measurement times. A fast and compact thermometer compatible with ultra-low temperatures and high magnetic fields while having sufficiently fine resolution to measure small samples will enable new calorimetric measurements in this difficult to access regime.