Thermal resolution (also referred to as temperature uncertainty) establishes the minimum discernible temperature change sensed by luminescent thermometers and is a key figure of merit to rank them. Much has been done to minimize its value via probe optimization and correction of readout artifacts, but little effort was put into a better exploitation of calibration datasets. In this context, this work aims at providing a new perspective on the definition of luminescence-based thermometric parameters using dimensionality reduction techniques that emerged in the last years. The application of linear (Principal Component Analysis) and non-linear (t-distributed Stochastic Neighbor Embedding) transformations to the calibration datasets obtained from rare-earth nanoparticles and semiconductor nanocrystals resulted in an improvement in thermal resolution compared to the more classical intensity-based and ratiometric approaches. This, in turn, enabled precise monitoring of temperature changes smaller than 0.1 °C. The methods here presented allow choosing superior thermometric parameters compared to the more classical ones, pushing the performance of luminescent thermometers close to the experimentally achievable limits.

热分辨率（也称为温度不确定性）确定了发光温度计感知的最小可辨别温度变化，并且是对其进行排名的关键品质因数。已经做了很多工作来通过探针优化和读数伪影的校正来最小化其价值，但很少努力更好地利用校准数据集。在这种情况下，这项工作旨在为使用过去几年出现的降维技术定义基于发光的温度测量参数提供一个新的视角。与更经典的基于强度的方法相比，对从稀土纳米颗粒和半导体纳米晶体获得的校准数据集应用线性（主成分分析）和非线性（t 分布随机邻域嵌入）变换可提高热分辨率和比率方法。这反过来又可以精确监测小于 0.1 °C 的温度变化。与更经典的方法相比，这里介绍的方法允许选择更好的温度测量参数，从而将发光温度计的性能推向接近实验可实现的极限。