Alternative slow neutron detection technologies are desired in response to needs by homeland security and radiation safety. Superheated droplet detectors (SDD) have the potential to be used in both situations because they have many distinct characteristics, such as photon insensitivity, passive operation, tissue-equivalent composition, isotropic response, flexible size, and low cost. A neutron detection system based on SDDs is developed in this research for simultaneous detection of fast neutrons and slow neutrons. The detection system is composed of two SDDs and an imaging-readout system. One of the SDDs is doped with 3.4\% $^6$LiCl, and the other one is a regular SDD. In the imaging-readout system of the detection system, machine learning algorithms are developed for accurate bubble counting. The algorithms show a much better accuracy and precision than traditionally used algorithms.
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Simultaneous fast and slow neutron detection with superheated droplet detector and real-time readout system