  Novel methods for rapidly estimating single-photon source (SPS) quality have
been promoted in recent literature to address the expensive and time-consuming
nature of experimental validation via intensity interferometry. However, the
frequent lack of uncertainty discussions and reproducible details raises
concerns about their reliability. This study investigates the use of data
augmentation, a machine learning technique, to supplement experimental data
with bootstrapped samples and quantify the uncertainty of such estimates. Eight
datasets obtained from measurements involving a single InGaAs/GaAs epitaxial
quantum dot serve as a proof-of-principle example. Analysis of one of the SPS
quality metrics derived from efficient histogram fitting of the synthetic
samples, i.e. the probability of multi-photon emission events, reveals
significant uncertainty contributed by stochastic variability in the Poisson
processes that describe detection rates. Ignoring this source of error risks
severe overconfidence in both early quality estimates and claims for
state-of-the-art SPS devices. Additionally, this study finds that standard
least-squares fitting is comparable to using a Poisson likelihood, and
expanding averages show some promise for early estimation. Also, reducing
background counts improves fitting accuracy but does not address the
Poisson-process variability. Ultimately, data augmentation demonstrates its
value in supplementing physical experiments; its benefit here is to emphasise
the need for a cautious assessment of SPS quality.
