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Producing a polarized lanthanum (La) target with high polarization and long relaxation time is crucial for realizing time-reversal violation experiments using polarized neutron beams. We use a LaAlO3 crystal doped with a small amount of Nd3+ ions for the polarized lanthanum target. Optimizing the amount of Nd3+ ions is considerably important because the achievable polarization and relaxation time strongly depend on this amount. We established a fundamental method to grow single crystals of Nd3+:LaAlO3 using an optical floating zone method that employs halogen lamps and evaluated the crystals with the dynamic nuclear polarization (DNP) method for polarizing nuclear spins. Two crystal samples were grown by ourselves and evaluated with the DNP at 1.3 K and 2.3 T for the first time, except for the target materials of protons. The enhancement of nuclear magnetic resonance signals for 139La and 27Al was successfully observed, and the enhancement factors were eventually 3.5 ± 0.3 and 13 ± 3 for the samples with Nd3+ ions of 0.05 and 0.01 mol. %, respectively. These enhancement factors correspond to absolute vector polarizations of 0.27% ± 0.02% (Nd3+ 0.05 mol. %) and 1.4% ± 0.3% (Nd3+ 0.01 mol. %). Although the obtained polarizations are still low, they are acceptable as a first step. The combination scheme of the crystal growth and the evaluation of the crystals is found to be effectively applicable for optimizing the amount of Nd3+ ions for improving the performance of the polarized target., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Solid-state nuclear track detectors (SSNTDs) are often used as ion detectors in laser-driven ion acceleration experiments and are considered to be the most reliable ion diagnostics since they are sensitive only to ions and measure ions one by one. However, ion pit analyses require tremendous time and effort in chemical etching, microscope scanning, and ion pit identification by eyes. From a laser-driven ion acceleration experiment, there are typically millions of microscopic images, and it is practically impossible to analyze all of them by hand. This research aims to improve the efficiency and automation of SSNTD analyses for laser-driven ion acceleration. We use two sets of data obtained from calibration experiments with a conventional accelerator where ions with known nuclides and energies are generated and from actual laser experiments using SSNTDs. After chemical etching and scanning the SSNTDs with an optical microscope, we use machine learning to distinguish the ion etch pits from noises. From the results of the calibration experiment, we confirm highly accurate etch-pit detection with machine learning. We are also able to detect etch pits with machine learning from the laser-driven ion acceleration experiment, which is much noisier than calibration experiments. By using machine learning, we successfully identify ion etch pits ∼105 from more than 10 000 microscopic images with a precision of ≳95%. A million microscopic images can be examined with a recent entry-level computer within a day with high precision. Machine learning tremendously reduces the time consumption on ion etch pit analyses detected on SSNTDs., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)