Machine‐Learning‐Assisted Onset‐Time Determination in Transient Luminescence Thermometry

Transient heating curves obtained from luminescence thermometry encode valuable information about nanoscale heat transport, yet extracting the onset time reliably remains challenging due to experimental noise and the need for manual signal‐processing pipelines. Here, we introduce a machine‐learning‐assisted framework for automated analysis of transient heating curves measured using Ln 3+ ‐doped upconverting nanoparticles. Neural‐network models spanning several architectural families are trained using a balanced dataset combining 822 experimental transients with 822 physically motivated synthetic curves designed to extend the diversity of heating dynamics beyond experimentally accessible variability. We show that predictive robustness is governed primarily by the diversity and physical plausibility of the training data rather than by architectural complexity alone. Relative to a conventional discrete wavelet transform reference procedure, the best‐performing models achieve median onset‐time errors on the order of 2 s, and once trained, determine the onset time with sub‐second latency per transient curve. The resulting framework enables fully automated, operator‐independent analysis of transient luminescence signals and provides a scalable strategy for extracting thermal metrics from time‐resolved spectroscopic data. More broadly, this approach establishes a pathway toward data‐driven analysis of dynamic thermal processes in nanoscale systems and intelligent materials with integrated sensing capabilities.