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We present two analysis techniques for distinguishing background eventsinduced by neutrons from photon signal events in the search for the rare$K^0_L\rightarrow\pi^0\nu\bar{\nu}$ decay at the J-PARC KOTO experiment. Thesetechniques employed a deep convolutional neural network and Fourier frequencyanalysis to discriminate neutrons from photons, based on their variations incluster shape and pulse shape, in the electromagnetic calorimeter made ofundoped CsI. The results effectively suppressed the neutron background by afactor of $5.6\times10^5$, while maintaining the efficiency of$K^0_L\rightarrow\pi^0\nu\bar{\nu}$ at $70\%$.

Suppression of Neutron Background using Deep Neural Network and Fourier Frequency Analysis at the KOTO Experiment