Nuclear Science and Techniques最新文献

In the research and development of new silicon pixel detectors, a collimated monoenergetic charged-particle test beam equipped with a high-resolution pixel-beam telescope is crucial for prototype verification and performance evaluation. When the beam energy is low, the effect of multiple Coulomb scattering on the measured resolution of the Device Under Test (DUT) must be considered to accurately evaluate the performance of the pixel chips and detectors. This study aimed to investigate the effect of multiple Coulomb scattering on the measured resolution, particularly at low beam energies. Simulations were conducted using Allpix(^2) to study the effects of multiple Coulomb scattering under different beam energies, material budgets, and telescope layouts. The simulations also provided the minimum energy at which the effect of multiple Coulomb scattering could be ignored. Compared with the results of a five-layer detector system tested with an electron beam at DESY, the simulation results were consistent with the beam test results, confirming the reliability of the simulations.

Neutron radiography is a crucial nondestructive testing technology widely used in the aerospace, military, and nuclear industries. However, because of the physical limitations of neutron sources and collimators, the resulting neutron radiographic images inevitably exhibit multiple distortions, including noise, geometric unsharpness, and white spots. Furthermore, these distortions are particularly significant in compact neutron radiography systems with low neutron fluxes. Therefore, in this study, we devised a multi-distortion suppression network that employs a modified generative adversarial network to improve the quality of degraded neutron radiographic images. Real neutron radiographic image datasets with various types and levels of distortion were built for the first time as multi-distortion suppression datasets. Thereafter, the coordinate attention mechanism was incorporated into the backbone network to augment the capability of the proposed network to learn the abstract relationship between ideally clear and degraded images. Extensive experiments were performed; the results show that the proposed method can effectively suppress multiple distortions in real neutron radiographic images and achieve state-of-the-art perceptual visual quality, thus demonstrating its application potential in neutron radiography.

The Moon provides a unique environment for investigating nearby astrophysical events such as supernovae. Lunar samples retain valuable information from these events, via detectable long-lived “fingerprint” radionuclides such as ({}^{60} hbox{Fe}). In this work, we stepped up the development of an accelerator mass spectrometry (AMS) method for detecting ({}^{60} hbox{Fe}) using the HI-13 tandem accelerator at the China Institute of Atomic Energy (CIAE). Since interferences could not be sufficiently removed solely with the existing magnetic systems of the tandem accelerator and the following Q3D magnetic spectrograph, a Wien filter with a maximum voltage of (pm,60,text {kV}) and a maximum magnetic field of 0.3 T was installed after the accelerator magnetic systems to lower the detection background for the low abundance nuclide ({}^{60} hbox{Fe}). A (1,upmu text {m}) thick Si(_{3})N(_{4}) foil was installed in front of the Q3D as an energy degrader. For particle detection, a multi-anode gas ionization chamber was mounted at the center of the focal plane of the spectrograph. Finally, an ({}^{60} hbox{Fe}) sample with an abundance of (1.125 times 10^{-10}) was used to test the new AMS system. These results indicate that ({}^{60} hbox{Fe}) can be clearly distinguished from the isobar ({}^{60} hbox{Ni}). The sensitivity was assessed to be better than (4.3 times 10^{-14}) based on blank sample measurements lasting (5.8) h, and the sensitivity could, in principle, be expected to be approximately (2.5 times 10^{-15}) when the data were accumulated for 100 h, which is feasible for future lunar sample measurements because the main contaminants were sufficiently separated.

The high energy cosmic-radiation detection (HERD) facility is planned to launch in 2027 and scheduled to be installed on the China Space Station. It serves as a dark matter particle detector, a cosmic ray instrument, and an observatory for high-energy gamma rays. A transition radiation detector placed on one of its lateral sides serves dual purpose, (i) calibrating HERD’s electromagnetic calorimeter in the TeV energy range, and (ii) serving as an independent detector for high-energy gamma rays. In this paper, the prototype readout electronics design of the transition radiation detector is demonstrated, which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip. The electronic performance of the prototype system is evaluated in terms of noise, linearity, and resolution. Through the presented design, each electronic channel can achieve a dynamic range of 0–100 fC, the RMS noise level not exceeding 0.15 fC, and the integral nonlinearity was <0.2%. To further verify the readout electronic performance, a joint test with the detector was carried out, and the results show that the prototype system can satisfy the requirements of the detector’s scientific goals.

Prompt radiation emitted during accelerator operation poses a significant health risk, necessitating a thorough search and securing of hazardous areas prior to initiation. Currently, manual sweep methods are employed. However, the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators. By leveraging advancements in machine vision technology, the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security. Given the criticality of personal safety for stranded individuals, search and security processes must be sufficiently reliable. To ensure comprehensive coverage, 180° camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range. The YOLOV8 network model was modified to enable the detection of small targets, such as hands and feet, as well as larger targets formed by individuals near the cameras. Furthermore, the system incorporates a pedestrian recognition model that detects human body parts, and an information fusion strategy is used to integrate the detected head, hands, and feet with the identified pedestrians as a cohesive unit. This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment, resulting in a notable improvement in the recall rate. Specifically, recall rates of 0.915 and 0.82 were obtained for Datasets 1 and 2, respectively. Although there was a slight decrease in accuracy, it aligned with the intended purpose of the search-and-secure software design. Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

The High Altitude Detection of Astronomical Radiation (HADAR) experiment, which was constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high-sensitivity advantages of focused Cherenkov detectors. Its objective is to observe transient sources such as gamma-ray bursts and the counterparts of gravitational waves. This study aims to utilize the latest AI technology to enhance the sensitivity of HADAR experiments. Training datasets and models with distinctive creativity were constructed by incorporating the relevant physical theories for various applications. These models can determine the type, energy, and direction of the incident particles after careful design. We obtained a background identification accuracy of 98.6%, a relative energy reconstruction error of 10.0%, and an angular resolution of 0.22(^circ) in a test dataset at 10 TeV. These findings demonstrate the significant potential for enhancing the precision and dependability of detector data analysis in astrophysical research. By using deep learning techniques, the HADAR experiment’s observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. In addition, our experiment offers a new approach for dealing with strongly connected, scattered data.

This paper presents a new technique for measuring the bunch length of a high-energy electron beam at a bunch-by-bunch rate in storage rings. This technique uses the time–frequency-domain joint analysis of the bunch signal to obtain bunch-by-bunch and turn-by-turn longitudinal parameters, such as bunch length and synchronous phase. The bunch signal is obtained using a button electrode with a bandwidth of several gigahertz. The data acquisition device was a high-speed digital oscilloscope with a sampling rate of more than 10 GS/s, and the single-shot sampling data buffer covered thousands of turns. The bunch-length and synchronous phase information were extracted via offline calculations using Python scripts. The calibration coefficient of the system was determined using a commercial streak camera. Moreover, this technique was tested on two different storage rings and successfully captured various longitudinal transient processes during the harmonic cavity debugging process at the Shanghai Synchrotron Radiation Facility (SSRF), and longitudinal instabilities were observed during the single-bunch accumulation process at Hefei Light Source (HLS). For Gaussian-distribution bunches, the uncertainty of the bunch phase obtained using this technique was better than 0.2 ps, and the bunch-length uncertainty was better than 1 ps. The dynamic range exceeded 10 ms. This technology is a powerful and versatile beam diagnostic tool that can be conveniently deployed in high-energy electron storage rings.

The coherent muon-to-electron transition (COMET) experiment is a leading experiment for the coherent conversion of (mu ^- textrm{N}rightarrow e^- textrm{N}) using a high-intensity pulsed muon beamline, produced using innovative slow-extraction techniques. Therefore, it is critical to measure the muon beam characteristics. We set up a muon beam monitor (MBM), where scintillating fibers woven in a cross shape were coupled to silicon photomultipliers to measure the spatial profile and timing structure of the extracted muon beam for the COMET. The MBM detector was tested successfully with a proton beamline at the China Spallation Neutron Source and took data with good performance in the commissioning run. The development of the MBM, including its mechanical structure, electronic readout, and beam measurement results, are discussed

Currently, three types of superconducting quadrupole magnets are used in particle accelerators: (cos {2theta }), CCT, and serpentine. However, all three coil configurations have complex spatial geometries, which make magnet manufacturing and strain-sensitive superconductor applications difficult. Compared with the three existing quadrupole coils, the racetrack quadrupole coil has a simple shape and manufacturing process, but there have been few theoretical studies. In this paper, the two-dimensional and three-dimensional analytical expressions for the magnetic field in coil-dominated racetrack superconducting quadrupole magnets are presented. The analytical expressions of the field harmonics and gradient are fully resolved and depend only on the geometric parameters of the coil and current density. Then, a genetic algorithm is applied to obtain a solution for the coil geometry parameters with field harmonics on the order of (10^{-4}). Finally, considering the practical engineering needs of the accelerator interaction region, electromagnetic design examples of racetrack quadrupole magnets with high gradients, large apertures, and small apertures are described, and the application prospects of racetrack quadrupole coils are analyzed.

Beams typically do not travel through the magnet centers because of errors in storage rings. The beam deviating from the quadrupole centers is affected by additional dipole fields due to magnetic field feed-down. Beam-based alignment (BBA) is often performed to determine a golden orbit where the beam circulates around the quadrupole center axes. For storage rings with many quadrupoles, the conventional BBA procedure is time-consuming, particularly in the commissioning phase, because of the necessary iterative process. In addition, the conventional BBA method can be affected by strong coupling and the nonlinearity of the storage ring optics. In this study, a novel method based on a neural network was proposed to determine the golden orbit in a much shorter time with reasonable accuracy. This golden orbit can be used directly for operation or adopted as a starting point for conventional BBA. The method was demonstrated in the HLS-II storage ring for the first time through simulations and online experiments. The results of the experiments showed that the golden orbit obtained using this new method was consistent with that obtained using the conventional BBA. The development of this new method and the corresponding experiments are reported in this paper.