3D track extraction from a fluorescent nuclear track detector via machine learning and an application to diagnostics of laser-driven ions.

We have developed an ion diagnostic method for laser-driven ion acceleration experiments that uses fluorescent nuclear track detectors (FNTDs). An FNTD records the particle tracks as color centers and does not require chemical etching, unlike CR-39 track detectors. The color centers are observed using a confocal laser microscope, and 3D particle tracks can be obtained by changing its focal position. The intensity of the color centers corresponds to the energy deposited by the ions. The nuclides of the ions can be determined from the intensity distribution of the color centers as a function of depth and the distance between the stopping point and the surface of the detector. To extract the intensity distribution, we must track the same ion tracks in the depth-layered microscopic images from the surface to the stopping point, even if they overlap with those of other ions. In addition, since an FNTD is sensitive not only to ions but also to electrons and photons, we must identify ion tracks among those from the latter particles. To analyze a statistical number of ion tracks, it is necessary to automate these processes. We have thus developed a method for automated ion detection and 3D tracking that relies on a support vector classifier and a kernelized correlation filter. This method was tested on a laser ion acceleration experiment performed using the J-KAREN-P laser. The method automatically detects ion tracks on FNTDs and tracks them in the depth direction. The training data are sampled from the Heavy-Ion Medical Accelerator in Chiba.