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6 pags., 3 figs., A machine learning model has been developed to search for events of production and decay of a hypertriton in nuclear emulsion data, which is used for measuring the binding energy of the hypertriton at the best precision. The developed model employs an established technique for object detection and is trained with surrogate images generated by Monte Carlo simulations and image transfer techniques. The first hypertriton event has already been detected with the developed method only with 10−4 of the total emulsion data. It implies that a sufficient number of hypertriton events will soon be detected for the precise measurement of the hypertriton binding energy.

Neutron imaging is a nondestructive inspection technique that has a wide range of applications. One of the important aspects of neutron imaging is achieving a micrometer-scale spatial resolution. The development of a high-resolution neutron detector is a challenging task. As one potential solution to this task, we investigate whether neutron detectors based on fine-grained nuclear emulsions are suitable for high-resolution neutron imaging applications. High track density is necessary to improve the quality of neutron imaging. However, the available track analysis methods are difficult to apply under high track density conditions. Simulated images are used to determine the required track density for neutron imaging. A track density of the order of [Formula: see text] tracks per [Formula: see text] is sufficient to utilize neutron detectors for imaging applications. Contrast resolution was also investigated for image datasets with various track densities and neutron transmission rates. Moreover, experiments were performed for the neutron imaging of gadolinium-based gratings with known geometries. The grating structures were successfully resolved. The calculated [Formula: see text] 10%–90% edge response using the grayscale optical images of the grating slit with a periodic structure of 9 [Formula: see text]m was [Formula: see text] [Formula: see text]m.

Fluorescent nuclear track detectors (FNTDs) based on Al${_2}$O${_3}$:C,Mg crystals are luminescent detectors that can be used for dosimetry and detection of charged particles and neutrons. These detectors can be utilised for imaging applications where a reasonably high track density, approximately of the order of 1 $\times$ $10^4$ tracks in an area of 100 $\times$ 100 $\mu$m$^2$, is required. To investigate the reusability of FNTDs for imaging applications, we present an approach to perform optical bleaching under the required track density conditions. The reusability was assessed through seven irradiation-bleaching cycles. For the irradiation, the studied FNTD was exposed to alpha-particles from an $^{241}$Am radioactive source. The optical bleaching was performed by means of ultraviolet laser light with a wavelength of 355 nm. Three dedicated regions on a single FNTD with different accumulated track densities and bleaching conditions were investigated. After every irradiation-bleaching cycle, signal-to-noise ratio was calculated to evaluate FNTD performance. It is concluded that FNTDs can be reused at least seven times for applications where accumulation of a high track density is required.

Hypertriton is the lightest hypernucleus and a benchmark in hypernuclear physics. However, it has recently been suggested that its lifetime and binding energy values may differ from the established values. To solve this puzzle, it is necessary to measure both values with a higher precision. For the precise measurement of the binding energy, we are aiming at developing a novel technique to measure the hypertriton binding energy with unprecedented accuracy by combining nuclear emulsion data and machine learning techniques. The analysis will be based on the J-PARC E07 nuclear emulsion data. Furthermore, a machine-learning model is being developed to identify other single and double-strangeness hypernuclei.