arXiv subjects

Yumiao Wang

Publications and source records attributed to Yumiao Wang.

4 recordsLinked to original sources

arXiv2026

Generation of continuous-wave laser light at 148.4 nm using cavity-enhanced second harmonic generation in $BaMgF_4$

We experimentally investigate the potential of $BaMgF_4$ crystals to create a continuous-wave (CW) solid state laser at the vacuum ultraviolet (VUV) wavelength of 148.4 nm via cavity-enhanced second harmonic generation. This investigation is motivated by the development of a nuclear optical clock based on a transition between the ground and isomeric state in the $^{229}Th$ nucleus. For this purpose, a $BaMgF_4$ crystal was grown, optically polished and periodically poled. The crystal was inserted into a power-enhancement cavity, resonant at the fundamental wavelength of 296.8 nm and the generated laser light at 148.4 nm was characterized. Within this proof-of-concept investigation, a VUV output power of typically ($16\pm1$) pW is obtained. This marks the first time that this type of crystal is used to generate VUV laser light. The experimental findings are compared to theoretical expectations and provide a clear path for future improvements.

physics.optics↗

arXiv2026

Enhanced Yield Rate of \textsuperscript{229m}Th via Cascade Decay in Storage Rings and Electron Beam Ion Traps

The low-energy nuclear isomeric state of \textsuperscript{229m}Th provides a unique bridge between nuclear and atomic physics, enabling applications such as nuclear clocks and precision metrology. However, efficient and controllable production of \textsuperscript{229m}Th remains a major experimental challenge. We propose an efficient scheme to produce the $^{229\mathrm{m}}$Th in storage rings (SRs) and electron beam ion traps (EBITs), using a cascade decay pathway. Highly charged ions are excited to higher nuclear states via nuclear excitation by inelastic electron scattering (NEIES) and nuclear excitation by electron capture (NEEC), followed by radiative or internal conversion cascades that populate the isomer. Our calculations demonstrate that, under typical SRs and EBITs conditions, optimized indirect excitation pathways significantly enhance \textsuperscript{229m}Th production rate. In particular, NEIES can provide an enhancement of up to four orders of magnitude through cascade de-excitation at high energies, while NEEC can contribute an additional enhancement of up to several tens of times. Such a significant increase in the \textsuperscript{229m}Th yield rate would facilitate its application in various nuclear photonics fields, especially in the development of atomic nuclear clocks.

nucl-th↗

arXiv2025

LCB-CV-UNet: Enhanced Detector for High Dynamic Range Radar Signals

We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence preserving solution to address the inherent challenges in handling HDR features. Then, we propose the Dual Hybrid Dataset Construction method to generate a semi-synthetic dataset, approximating typical HDR signal scenarios with adjustable target distributions. Simulation results show about 1% total detection probability improvement with under 0.9% computational complexity added compared with the baseline. Furthermore, it excels 5% over the baseline at the range in 11-13 dB signal-to-noise ratio typical for urban targets. Finally, the real experiment validates the practicality of our model.

eess.SP↗

arXiv2023

Machine learning method for $^{12}$C event classification and reconstruction in the active target time-projection chamber

Active target time projection chambers are important tools in low energy radioactive ion beams or gamma rays related researches. In this work, we present the application of machine learning methods to the analysis of data obtained from an active target time projection chamber. Specifically, we investigate the effectiveness of Visual Geometry Group (VGG) and the Residual neural Network (ResNet) models for event classification and reconstruction in decays from the excited $2^+_2$ state in $^{12}$C Hoyle rotation band. The results show that machine learning methods are effective in identifying $^{12}$C events from the background noise, with ResNet-34 achieving an impressive precision of 0.99 on simulation data, and the best performing event reconstruction model ResNet-18 providing an energy resolution of $σ_E<77$ keV and an angular reconstruction deviation of $σ_θ<0.1$ rad. The promising results suggest that the ResNet model trained on Monte Carlo samples could be used for future classifying and predicting experimental data in active target time projection chambers related experiments.

physics.ins-det↗