Fig. 2: Machine learning assisted measurement of g(2)(0).
From: Machine learning assisted quantum super-resolution microscopy
a Schematics of the HBT interferometer. Labels:Â DM dichroic mirror, LPFÂ long-pass filter, BSÂ beam splitter, D1/D2Â detectors. b Schematics of the CNN regression network. The input layer takes in sparse HBT histograms. The total number of events, Nevents, of the histogram is concatenated to the output of the feature learning part and used as a regularization term. c, d Regression plot (predicted vs expected \({g}^{\left(2\right)}\left(0\right)\) values) for L-M fitting (c) and CNN regression of \({g}^{\left(2\right)}\left(0\right)\) (d) from 5s datasets. Dots show the average predicted \({g}^{\left(2\right)}\left(0\right)\) value, while error bars show the standard deviation of the predicted value over all the 5s datasets acquired for a given emitter.
