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Luminescence thermometry has been extensively exploited in the last decadesboth from the fundamental and applied point of views. The application ofphotoluminescent nanoparticles on the microscopic level based on rare-earthdoped (RED) nanostructures is yet a challenge. Distinct underlying physicalmechanisms in the RED nanomaterials have been exploited, such as intensityratio between radiative transitions associated with thermally coupled energylevels, energy peak and lifetime of an excited state variations with thetemperature. The drawbacks of such systems are the relatively low thermalsensitivity (Sr), and the large temperature uncertainty. To overcome that,several research groups have been seeking new functionalized materials. Themajority of the efforts have been directed towards increasing Sr with recordaround 10 %{\deg}C-1, which is, however, considered unsatisfactory. We proposethe use of an automated machine learning tool to retrieve an ideal pipelineimproving the response of photoluminescence thermometers. As aproof-of-concept, we used Nd3+-doped YAG nanoparticles, excited at 760 nm, andthe photoluminescence spectra in the range from 860 nm to 960 nm as inputparameters. In addition to the improvement in the accuracy (> 5.5x overtraditional methods), the implementation is very simple, without therequirement of any deconvolution procedure or knowledge of any underlyingphysical mechanism. Our findings demonstrate that this approach is resilient tonatural variances across various spectral acquisitions, which may otherwiselead to an inaccurate estimation of temperature, opening the door for real-timeapplications. Our open-source code is designed to be accessible to individualswithout programming experience.

Towards accurate real-time luminescence thermometry: an automated machine learning approach