Efficient Analysis of Photoluminescence Images for the Classification of Single-Photon Emitters

Solid-state single-photon emitters (SPE) are a basis for emerging technologies such as quantum communication and sensing. SPE based on fluorescent point defects ubiquitous in semiconductors insulators, new systems with desirable properties information science may exist among the vast number of unexplored defects. However, characterization typically relies time-consuming techniques identifying sources by eye photoluminescence (PL) images. This manual strategy is bottleneck discovering SPE, motivating more efficient method characterizing PL Here, we present quantitative using image analysis regression fitting to automatically identify Gaussian images classify them according their stability, shape, intensity relative background. We demonstrate emitter classification nanodiamond arrays hexagonal boron nitride flakes. Adaptive criteria detect both samples despite variation intensity, background features. The detection can be tuned specific material experimental setups accommodate diverse SPE.