Processing calibration data of low-temperature thermometer based on clustering algorithm.

Abstract

The scarcity of cryogenic thermometers often stems from their high cost and lengthy lead times for calibration. Establishing an in-lab temperature calibration system is necessary to quickly make use of uncalibrated sensors or self-made sensors. This paper introduces a straightforward and high-accuracy thermometer calibration system. By employing copper screws as thermal links between the sensor platform and the cryogen-free refrigerator, temperature oscillation on the sensor platform is suppressed to a few millikelvins. In addition, this paper presents a data processing model based on clustering algorithms. These algorithms sort and group data based on distance, which is similar to human visual judgment of data. This paper discusses the parameter optimization process of the clustering algorithm to interpret the automated data process. The cryogenic temperature sensors calibrated by this system exhibited high accuracy, with relative errors of less than 1% compared to standard thermometers. Moreover, automatically processing calibration data from two uncalibrated thermometers takes just over 10min, highlighting the effectiveness of this calibration system.