Fig. 1: Distinguishing SU(N) fermions based on spin multiplicity by machine learning.
From: Heuristic machinery for thermodynamic studies of SU(N) fermions with neural networks
a Schematic of preparing SU(N) gases in the optical dipole trap (ODT). The momentum distribution of the SU(N) Fermi liquid of 173Yb atoms with tunable spin configuration is recorded. The collected datasets are then fed into the NN as the input images for classification. b Examples of single experimental images of SU(N) gases. c Radially averaged optical density (OD) profiles in different SU(N) gases. The shaded region represents the fluctuation of the density profiles. d Experimental images of SU(N) gases are loaded into the neural network with one single convolutional layer. The black line and window represent how the kernel slides across the image. The output layer classifies the image into one of the class (i.e., SU(1), SU(2), SU(5), SU(6)) resulting in a classification accuracy around 94%. For each input image, NN outputs probabilities of different SU(N) with the highest value of the correct class. The output probabilities of NN are averaged over the test dataset.
