arXiv subjects

Ningtao Zhang

Publications and source records attributed to Ningtao Zhang.

5 recordsLinked to original sources

arXiv2026

A cryogenic gas target for high-intensity radioactive ion beam production at HIRFL-RIBLL

A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82--86 K during beam irradiation and operating pressures up to 1000 mbar. The system was used to produce $^{7}\mathrm{Be}$, $^{16}\mathrm{N}$, and $^{15}\mathrm{O}$ RIBs via the $^{1}\mathrm{H}(^{7}\mathrm{Li}, ^{7}\mathrm{Be})n$, $^{2}\mathrm{H}(^{15}\mathrm{N}, ^{16}\mathrm{N})p$, and $^{1}\mathrm{H}(^{15}\mathrm{N}, ^{15}\mathrm{O})n$ inverse kinematics reactions, yielding purities of 85\%, 99\%, and 95\%, with intensities of $1.02\times10^{6}$, $2.7\times10^{5}$, and $1.0\times10^{5}$ pps, respectively. A $^{93m}\mathrm{Mo}$ isomer beam was also produced via the $\mathrm{^4He(^{94}Zr,} 5n)^{93m}\mathrm{Mo}$ reaction, achieving an intensity of $5.38\times10^{3}$ pps and a purity of 20\% (which can be further improved to $\sim$50\% with offline time-of-flight gating). By delivering a broader range of high-intensity secondary RIBs, this setup establishes a robust platform at RIBLL for low- and medium-energy nuclear astrophysics and reaction studies.

physics.ins-det↗

arXiv2026

Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

In modern nuclear physics experiments, identifying events of interest is challenging for nuclear reaction studies with the active target Time Projection Chamber (TPC). In this work, machine learning techniques are employed to analyze the complex data of the 12C + 12C fusion reaction from a TPC named MATE (multi-purpose active-target time projection chamber for nuclear experiments). Specifically, we successfully applied Residual Neural Network (ResNet-50, ResNet-34 and ResNet-18) and Visual Geometry Group (VGG-19) to classify elastic scattering and fusion reaction events from the 12C + 12C reaction. The classification results of the four models are nearly identical, with accuracies of approximately 97% for the simulated data and 90% for the experimental data. Moreover, these approaches successfully identify some events that are misclassified by traditional methods. These models are also applied to classify events from different fusion reaction channels, with classification accuracies of approximately 95% on simulated data. In addition, a Convolutional Neural Network (CNN) model is developed to reconstruct the reaction vertex, providing an alternative strategy for vertex reconstruction. These results indicate that machine learning techniques can effectively classify reaction events from different channels and reconstruct the reaction vertex, thereby paving the way for future analyses of complex nuclear reaction data.

cs.LG↗

arXiv2025

Measurement of the isoscalar giant monopole resonance in $^{86}$Kr via deuteron inelastic scattering using an active target CAT-M

Deuteron inelastic scattering on $^{86}$Kr was measured in inverse kinematics with the gaseous active target CAT-M, as part of a systematic investigation aimed at determining the nuclear matter incompressibility. The isoscalar monopole strength distribution was extracted via multipole decomposition analysis, and the energy of the isoscalar giant monopole resonance was determined to be 17 $\pm$ 1 MeV. The nuclear incompressibility of $^{86}$Kr and the isospin-dependent term of the nuclear matter incompressibility are discussed.

nucl-ex↗

arXiv2019

High Speed Mid-Infrared Interband Cascade Photodetector Based on InAs/GaSb Type-II Superlattice

High speed mid-wave infrared (MWIR) photodetectors have applications in the areas such as free space optical communication and frequency comb spectroscopy. However, most of the research on the MWIR photodetectors is focused on how to increase the quantum efficiency and reduce the dark current, in order to improve the detectivity (D*), and the 3dB bandwidth performance of the corresponding MWIR photodetectors is still not fully studied. In this work, we report and characterize a MWIR interband cascade photodetector based on InAs/GaSb type-II superlattice with a 50% cutoff wavelength at ~5.3 um at 300 K. The 3 dB cutoff frequency is 2.4 GHz at 300 K, for a 40 μm circular diameter device under -5 V applied bias. Limitations on the detector high speed performance are also discussed.High speed mid-wave infrared (MWIR) photodetectors have applications in the areas such as free space optical communication and frequency comb spectroscopy. However, most of the research on the MWIR photodetectors is focused on how to increase the quantum efficiency and reduce the dark current, in order to improve the detectivity (D*), and the 3dB bandwidth performance of the corresponding MWIR photodetectors is still not fully studied. In this work, we report and characterize a MWIR interband cascade photodetector based on InAs/GaSb type-II superlattice with a 50% cutoff wavelength at ~5.3 um at 300 K. The 3 dB cutoff frequency is 2.4 GHz at 300 K, for a 40 um circular diameter device under -5 V applied bias. Limitations on the detector high speed performance are also discussed.

physics.app-ph↗

arXiv2019

InGaAsP/InP uni-travelling-carrier photodiode at 1064nm wavelength

High-speed back-illuminated uni-traveling-carrier photodiodes at 1064nm were demonstrated grown on InP with 3dB bandwidth of 17.8 GHz at -5 V bias, using InGaAsP as absorption layer. PDs with 40um diameter deliver RF output power levels as high as 19.5 dBm at 13 GHz. This structure can achieve low dark current density of 10 nA per cm2 at -5V bias and quantum efficiency of 45.2% at 1064nm. An analytical model based on S-parameter fitting was built to extract parameter to access the bandwidth limiting factors.

physics.app-ph↗