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