###### Abstract  
In neutron transmission spectroscopic imaging, the transmission spectrum of each pixel on a two-dimensional detector is analyzed and the real-space distribution of microscopic information in an object is visualized with a wide field of view by mapping the obtained parameters. In the analysis of the transmission spectrum, since the spectrum can be classified with certain characteristics, it is possible for machine learning methods to be applied. In this study, we selected the subject of solid-liquid phase fraction imaging as the simplest application of the machine learning method. Firstly, liquid and solid transmission spectra have characteristic shapes, so spectrum classification according to their fraction can be carried out. Unsupervised and supervised machine learning analysis methods were tested and evaluated with simulated datasets of solid-liquid spectrum combinations. Then, the established methods were used to perform an analysis with actual measured spectrum datasets. As a result, the solid-liquid interface zone was specified from the solid-liquid phase fraction imaging using machine learning analysis. keywords: neutron transmission spectroscopic imaging; machine learning analysis; solid-liquid phase fraction; solidification process; lead-bismuth eutectic alloy +
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analysis for each pixel, so it can be expected that the efficiency will be greatly improved, especially in the case of spectroscopic/energy-dependent imaging with a significant number of pixels. However, the issue in this case is the collection of a large number of spectra with a wide variety of microscopic structures needed to build the ML model. In other words, it is necessary to collect a number of Bragg-edge spectrum patterns of the solid material with various different microstructures, which is the main subject in NTSI analysis. Bragg-edge patterns reflecting various material microstructures can be calculated, for example, using RITS (Rietveld Imaging of Transmission Spectra) code [6]. However, it is difficult to calculate all types of microstructures as training spectra; moreover, there are patterns that cannot be calculated with analysis codes such as RITS. Therefore, we considered building an ML model for experimental analysis using the experimentally measured Bragg-edge spectra as training data for the ML. Our choice as the first trial for such ML analysis was the imaging of solid-liquid interfaces in motion. When the solid-liquid interface in the solidification process is measured by neutron imaging, due to the relationship between the measurement time and the solidification velocity, a zone appears where the solid and liquid phases coexist between the 100% solid-phase and 100% liquid-phase regions. In this study, we applied ML to the solid-liquid phase fraction analysis of this solid-liquid coexisting zone. In this case, the analysis was not used for the microstructure of the solid phase itself directly, so it was easy to apply ML. In addition, various Bragg-edge data can be collected using the solid-phase spectrum of the entire solidified sample. From the above, it is considered that the analysis of the solid-liquid phase fraction is optimal for evaluating the applicability of the ML analysis method to NTSI as a first step. In this study, lead-bismuth eutectic alloy (LBE), which our group has experience in analyzing [17], was used as a sample for imaging the solidification process. LBE is expected to be used as a coolant and spallation target for next-generation nuclear reactors (accelerator-driven subcritical reactors), and it is required for evaluating the heat transfer coefficient by determining the moving velocity of the solid-liquid interface [18; 19; 20]. As it is difficult to analyze the solid-liquid phase fraction of neutron transmission spectra with high accuracy for LBE due to its complicated crystal phase [21], this is considered an appropriate application of ML analysis.