Although high-speed waveform sampling has advanced high-precision timing, prohibitive data volumes and computational complexity have largely restricted these methods to laboratory-scale research. This study investigates data processing and digital timing algorithms to achieve an average per-channel time resolution of 68 ps and an overall coincidence time spectrum of 187 ps full width at half maximum (FWHM) in a 64-channel proof-of-concept. The optimized parameters are subsequently deployed in a 2048-channel system. A genetic algorithm is trained on offline data to determine the optimal filtering and timing parameters specific to each channel. These parameters are then validated during online application. Achieving an average per-channel time resolution of 68 ps and an overall coincidence time spectrum of 187 ps FWHM in a 64-channel proof-of-concept, with the optimized parameters subsequently deployed in a 2048-channel system. Online measurement of positron burst annihilation lifetime spectra has been achieved in a system comprising 2048 channels, enabling effective measurement and differentiation of standard samples such as metallic iron and polymer PC.

The issue of pulse pile-up at high count rate can reduce the signal-to-noise ratio of medical imaging, leading to imaging blur, spectral distortion, and low timing resolution. The pile-up separation method has emerged as a corrective measure for pile-up signals, allowing for the effective utilization of particle information to improve the count rate. Consequently, this method has increasingly supplanted the rejection method as the preferred approach for pile-up processing. However, the majority of current pile-up separation methods rely on exponential fitting, which poses challenges for real-time processing in field-programmable gate arrays (FPGA). To address this limitation, this article proposes a separation method based on trapezoidal shaping, which utilizes symmetrical rising and falling edge data of trapezoidal signal, along with flat top data, accurately identifies and separates pile-up signals without relying on complex fitting operations. This algorithm is computationally simple and very suitable for implementation within FPGA to have real-time processing capabilities. In the experimental testing of radioactive sources using PET detectors, the proposed method can effectively enhance the flood map and energy spectrum in the presence of severe pile-up signals. Within the tested count rate range of 207 kcps to 6.71 Mcps, the proposed method not only improves energy resolution but also effectively increases count rate by 4.8 %-16.8 % compared to rejection method. Thus, the method proposed can provide a new real-time separation idea for the pile-up signal processing of PET detector at high count rate, and may also serve a viable solution for pile-up signal processing in other application domains.

Photon-Counting Detector Computed Tomography (PCD-CT) boasts excellent spectral utilization capability. Combined with material decomposition methods, it enables the calculation of the effective atomic number ($Z_{eff}$) and density ($\rho$) of scanned materials. Traditional material decomposition methods derive $Z_{eff}$ and $\rho$ from physical models using either the basis material model or the dual-effect model. However, these methods generally fail to meet high-precision decomposition requirements due to model approximations and suffer from the limitation of severe noise amplification in $Z_{eff}$ images. This study proposes a physics-constrained deep learning network that achieves high-precision joint estimation of $Z_{eff}$ and $\rho$ by dynamically modeling nonlinear X-ray interactions. A Multilayer Perceptron (MLP) is employed to construct dynamic compensation functions dependent on energy ($E$) and $Z_{eff}$ for the photoelectric effect exponent and Compton scattering model in the X-ray interaction model. The decomposition network adopts Swin-Unet as its backbone and utilizes a hybrid loss function, which consists of L1 loss and SSIM loss for $Z_{eff}$/$\rho$, as well as a physics-informed loss derived from the L1 loss between the predicted $Z_{eff}$/$\rho$ and the monoenergetic linear attenuation coefficient images generated by the optimized X-ray model. This design allows the network to simultaneously learn data-driven features and physical principles. Comparative experiments were conducted on a PCD-CT system between the proposed method and four methods (Lan, U-Net, Butterfly-net, and Swin-Unet without physical constraints). The results demonstrate that: for standard materials, the proposed method achieves a Mean Absolute Percentage Error (MAPE) below 5% for both $Z_{eff}$ and $\rho$ decomposition, with superior Noise Power Spectrum (NPS) performance; for biological samples including freshwater crayfish and mouse, the $Z_{eff}$ images generated by the proposed method exhibit higher Multi-Exposure Fusion Structural Similarity Index (MEF-SSIM), reaching 0.9559 for crayfish and 0.8950 for mouse. The method also demonstrates superior detail recovery capability in the restoration of the speckled tissue structure of crayfish and the tissue regions of mouse. By incorporating constraints from monoenergetic images generated based on the dynamic X-ray interaction model, the network effectively learns the nonlinear decomposition process of $Z_{eff}$ within a data-driven framework. The proposed method improves decomposition accuracy while reducing decomposition noise and enhancing the quality of decomposed images.

Abstract A comparative study of the gamma-ray response of Daya Bay gadolinium-loaded liquid scintillator (Gd-LS), JUNO liquid scintillator (LS), and the commercial reference scintillator EJ-309 was carried out using a compact 2-inch photomultiplier-tube-based detector system. Measurements were performed under identical conditions with 137 Cs, 22 Na, and 241 Am radioactive sources. The measured spectra are dominated by Compton continua, and the Compton-edge positions were extracted from the smoothed spectra using a first-derivative method. Based on the 137 Cs spectra, the relative light yields of Daya Bay Gd-LS and JUNO LS were determined to be (60.7 ± 0.2)% and (63.7 ± 0.2)% of that of EJ-309 LS, respectively, where the uncertainty reflects the variation associated with the Compton-edge extraction. Energy calibration based on the extracted Compton-edge positions shows approximately linear energy-ADC relationships for the three scintillators under the present detector and readout configuration. Although the LAB-based scintillators provide lower light output than EJ-309 LS, they show stable gamma-ray response in the present compact detector system and may serve as useful candidates for further radiation-detection studies beyond large-volume neutrino experiments.

Abstract A room-temperature method is developed to measure the intrinsic secondary electron yield (SEY) of gases and liquids physiosorbed on a metal surface, overcoming the limitation of the traditional requirement for cryogenic cooling. By fitting the time-resolved SEY during initial film growth to a derived exponential saturation model, the bulk SEY of the condensed medium is extracted. The method is validated on a Cu substrate for N 2 and CO 2 , yielding SEY curves and fitting parameters in close agreement with cryogenic reference data. The deviations observed for H 2 O are attributed to structural differences between room-temperature adsorption and cryogenic ice, and the SEY spectrum of C 2 H 5 OH is reported for the first time. The technique further serves as a highly sensitive probe for in-situ film thickness measurement. The method can serve as a convenient and inexpensive tool for monitoring nanofilm growth on metal surfaces.

What is believed to be a new method for generating high-resolution, high-flatness electro-optic frequency combs (EOFCs) based on multi-frequency small-signal modulation with a Single-driver Mach-Zehnder modulator (MZM) biased at its minimum-transmission point. This approach suppresses the optical carrier and even-order sidebands while minimizing the distortion from higher-order odd sidebands. Two EOFCs with repetition rates of 1 MHz and 1.000025 MHz were generated, each containing approximately 8000 comb lines with <1 dB local flatness (40 MHz optical spectral span). These combs were employed in a dual-comb spectroscopy system to measure the molecular absorption near P (10) rotational transition of the H13C14N. In addition, the proposed EOFC was applied to the measurement of stimulated Brillouin scattering (SBS) in a single-mode optical fiber, demonstrating its capability for high-resolution spectral characterization. The experimental results confirmed the effectiveness and versatility of the proposed EOFC scheme for precision spectroscopic applications.

Nanocrystalline soft magnetic alloy (MA) cores are essential magnetic loading components in proton/heavy-ion synchrotron rf cavities. While conventional MA cores fabricated from 16 μm ribbons offer reliable performance, further enhancing their shunt impedance and thermal stability to meet the demands of next-generation high-power synchrotrons presents significant challenges. Although the use of thinner ribbons (e.g., 13 μm) can improve high-frequency magnetic properties, their widespread application is hindered by manufacturing difficulties and reduced packing factor consistency. To address this, we propose an embedded MA core design that strategically integrates 13 μm ribbons in specific radial regions alongside normal 16 μm ribbons. This approach not only increases shunt impedance but also optimizes the electromagnetic field distribution, thereby reducing peak surface temperature and improving thermal stability. This paper first elucidates the physical mechanism through which the embedded structure improves temperature uniformity. Additionally, a two-dimensional heat transfer numerical model is developed and experimentally validated to assess the thermal performance of the embedded MA core under actual operating conditions. Experiments are conducted and the results demonstrate the embedded core’s peak surface temperature is reduced by 2.5 °C compared to a normal MA core. Finally, parameter optimization of the embedded structure is conducted, demonstrating that optimizing the radial position, volume ratio, and thickness of thinner ribbons can achieve a temperature reduction of approximately 6 °C and a 12% impedance increase. The findings of this study provide a theoretical foundation for the performance optimization of MA cores in high-power synchrotrons.

This study proposes a novel focused collimator-based Compton Backscatter Imaging (CBI) system designed to overcome the inherent trade-off between scattered photon collection efficiency and defect detection sensitivity in sub-surface non-destructive testing. Specifically, the proposed system is tailored for two-dimensional (2D) backscatter imaging focused exclusively on a micro-focal Region of Interest (ROI) at a fixed depth. Comprising concentrically arranged annular rings, the collimator is optimized to efficiently collect scattered photons from a targeted ROI (approximately 5 mm in depth) while effectively suppressing background noise from non-focal layers. A dedicated experimental prototype was constructed to validate the system’s depth sensitivity and thickness detection limits. Experimental results verify that the system maintains high sensitivity across varying depths: micro-defects with a thickness as small as 0.2 mm are reliably identified at depths up to 30 mm. Furthermore, the system exhibits robust deep-penetration capability, successfully resolving defects with a thickness of approximately 0.4 mm even at a depth of 50 mm. The effective focusing range is determined to be approximately 5 mm, within which optimal imaging clarity and peak Contrast-to-Noise Ratio (CNR) are achieved. A critical finding reveals that the combined effects of incident beam attenuation and multiple scattering constitute significant noise sources, ultimately limiting the detection of minute defects at greater depths. This work provides a viable approach for high-sensitivity, localized 2D non-destructive inspection of aerospace composites.

To address the imaging requirements of high-energy X-ray systems, in this study, a novel multirow line-array X-ray detector system is proposed for mitigating severe scattering artifacts and the low scanning efficiency in high-energy X-ray imaging. The system integrates three key components: (1) a front-end detector module featuring an 8 × 128 CWO scintillator-photodiode array for efficient X-ray-to-light conversion, (2) low-noise readout electronics with multichannel analog-to-digital converters and low-voltage differential signaling (LVDS)-based cascaded transmission architectures for synchronized signal digitization, and (3) a real-time data acquisition system that aggregates multimodule data via LVDS links and processes it through fiber-optic channels. GEANT4 Monte Carlo simulations are conducted using both a 7 × 7 unit-cell model for energy-response mapping and a full-scale 8 × 128 model for practical configuration validation. The experimental measurements show good agreement with the simulation results in terms of energy response and crosstalk suppression trends, indicating effective mitigation of scatter-induced performance degradation. The system demonstrates robust detection performance under high-energy X-ray irradiation. This study presents a scalable detector architecture that successfully balances detection efficiency and scanning speed, providing a practical and optimized solution for high-energy X-ray imaging in industrial and security inspection applications.

Dual-ended readout monolithic PET detectors provide high detection efficiency, DOI capability, and reduced edge-related positioning degradation, but require readout electronics capable of channel compression, dual-ended event association, and scalable event processing. This work presents a hierarchical FPGA-based electronics architecture with discrete analog frontend readout for a dual-ended monolithic PET detector. The system consists of signal acquisition, module-level preprocessing, and centralized processing layers. Row-column summation multiplexing is used to reduce the SiPM energy-readout channels, while FPGA-based processing performs energy extraction, adaptive-threshold position calculation, top/bottom event matching, dualended event fusion, and full-ring coincidence processing. Experimental results show that the proposed readout scheme reduces the effective energy-channel count by 83.3%. The detector achieves an energy resolution of 12.20% FWHM at 511 keV, a transverse spatial resolution of 0.97 mm, and a DOI resolution of 1.39 mm. The preprocessing stage provides a valid dual-ended event output rate of approximately 1 Mcps. An electrical split-delay benchmark was also performed to verify the FPGA-based timestamp extraction path; this benchmark is not interpreted as the timing resolution of the complete detector module. These results demonstrate that the proposed architecture provides a feasible and scalable readout solution for dual-ended monolithic PET detector development.

Objective. Spectral CT and material decomposition methods are crucial for precise material identification and quantitative composition analysis in preclinical research and clinical diagnosis. The empirical material decomposition method is widely used for its straightforward modeling approach, independence from spectral and detector response knowledge, and operational convenience. However, this method has limited decomposition accuracy and its precision depends on the choice of calibration phantoms. Approach. To address these issues, we propose an empirical correction decomposition method (ECDM). The innovation of this method lies in its ability to conveniently estimate and correct empirical decomposition errors using a specially designed calibration phantom. First, the specially designed calibration phantom for ECDM undergoes empirical decomposition initially to establish the relationship between decomposition errors and decomposition values. Then, ECDM estimates and corrects the error of empirical decomposition values. Main results. In the phantom experiments, ECDM improves the decomposition accuracy of empirical methods, effectively reducing the different decomposition errors caused by four different sizes of calibration phantoms from a maximum of 144% to within 25%. In the mouse experiments, ECDM achieves accurate quantification of contrast agents in biological tissues, outperforming the other two methods. The absolute error percentages of ECDM in the decomposition results of the two standard iodine solutions are both less than 5%. Significance. ECDM significantly improves decomposition accuracy and reduces the impact of the size of the empirical calibration phantom. Overall, our method based on spectral CT is very convenient and practical for the quantitative measurement in biomedical applications.

Dual-comb spectroscopy (DCS) is a powerful Fourier-transform spectroscopic technique that provides high-speed, high-resolution, and broadband measurements without moving parts. However, the high peak power of mode-locked pulses can limit the photodetector’s dynamic range, resulting in a low signal-to-noise ratio (SNR) per acquisition. While coherent averaging can improve SNR, it sacrifices temporal resolution and demands stringent system stability. Here, we introduce a new approach to enhance SNR by using phase-patterned higher-repetition-rate combs. We reinterpret the self-imaging process of comb spectrum from a new perspective on mode interference among sub-pulse trains. As a proof-of-concept, we densified two 250-MHz frequency combs to 12.5-MHz mode spacings via phase modulation and performed DCS on an H 13 C 14 N gas cell, and compared the results with an emulated conventional 12.5-MHz DCS, demonstrating a 17-fold increase in mode amplitude. This concept is expected to be combined with ultra-high repetition rate combs, such as microcombs, and thereby deployed in practical applications that typically require spectral sampling spacings from hundreds of MHz to GHz range.

Objective.Achieving a higher signal-to-noise ratio gain or direct positron emission imaging by improving the coincidence time resolution of time-of-flight positron emission tomography (PET) systems is paramount for many advanced clinical PET imaging applications. This places greater demands on the timing performance of all PET system components.Approach.An effective method for enhancing detector time resolution is to use microchannel plate photomultiplier tubes (MCP-PMTs) for prompt Cherenkov photon detection. In this study, we developed a dual-module PET imaging experimental platform. This platform uses two 8 × 8-anode Cherenkov radiator-integrated window MCP-PMTs, which was developed in-house. It also uses multi-channel electronics system, which was also developed in-house. We designed specific calibration and correction methods for the platform to meet the experimental requirements.Main results.Based on this dual-module platform, a total Full Width at half maximum of 203.6 ps (σ= 86.5 ps) was achieved. Multi-component analysis of the time spectrum was performed based on the interaction positions of annihilation photon pairs in different coincidence events. Imaging experiments were conducted using various types of radiation sources and phantoms. Spatial resolution was evaluated, confirming the platform's ability to distinguish 4 mm spots in Derenzo-like phantoms. Furthermore, preliminary improvements in image quality were verified when incorporating TOF information into the dual-module platform.Significance.In this study, we achieved the first experimental verification of module-level PET imaging based on the detection of Cherenkov light. This has overcome the limitations of single-pixel detectors in previous related research and reduced the radiation dose and acquisition time required for imaging by nearly 10 times. Additionally, this study evaluated the potential of a dual-module platform for achieving higher temporal performance and direct imaging, elucidated the importance of developing PbF2integrated window multi-anode MCP-PMTs, and provided experimental evidence for future performance enhancements in Cherenkov light detection-based PET systems.

The radiation imaging and intensity quantification of radioactive material is attracting increasing attention in numerous applications including radiological source investigation, radiation safety, nuclear security, nuclear facility maintenance and decommissioning. Here, quantitative intensity measurement of the far-field radioactive source is achieved by gamma-ray imaging, which is based on the conventional mask-antimask coded aperture approach. A multi-sensor radiation imaging system that fuses gamma-ray images, optical pictures, and 3D point clouds into a single vision is what we have created. Without the use of a mobile platform for numerous measurements or trajectory data, it is possible to simultaneously and in real time obtain the intensity and distribution of radioactive sources. In order to demonstrate the exceptional noise-resistant nature of the proposed quantitative gamma-ray imaging technique in the presence of interfering radiation, we present experimental results of point-like sources and actual nuclear power plant scenarios. This encourages further possibilities for widespread coded aperture applications. Incidentally, the system we designed offers a highly promising solution for the upgrade of existing coded aperture cameras.

Micro X-ray fluorescence (mu XRF) is a widely used technique for surface elemental analysis in the field of archaeology. However, conventional mu XRF imaging systems face significant challenges when applied to curved relics, particularly in achieving large-area, high-resolution elemental imaging. To address this limitation, we have developed a surface-adaptive mu XRF imaging system that employs a robotic arm as the motion platform. This setup ensures a constant working distance and a fixed X-ray incidence angle during the scanning process. We conducted a series of mu XRF imaging experiments on objects with various surface geometries. Notably, the scanning results of a curved unearthed relic demonstrate that, in regions with significant surface height variation, the surface-adaptive mu XRF system provides substantially higher spatial resolution compared to conventional flat scanning. Our method enables high-precision, large-area mu XRF imaging on irregular surfaces, significantly broadening the applicability of mu XRF in archaeological elemental analysis. It is anticipated that the surface-adaptive mu XRF imaging technique will play an increasingly important role in future archaeological research.

The nuclide 113Cd is sensitive to thermal neutrons. Most of the gamma rays generated from 113Cd(n, gamma)114Cd reactions have an energy of 558 keV. CdZnTe detectors contain Cd-113. Their good energy resolution for gamma rays ensures that they can identify the thermal neutrons by detecting the prompt gamma rays. Planar CdZnTe detectors are easy to manufacture and therefore cheap. Nonetheless, they have not yet been utilized in thermal neutron detection since their spectra lack the 558 keV photopeak, which is essential for identifying neutron capture events. In this paper, a 22 mmx22 mmx0.5 mm planar CdZnTe detector was used to detect the thermal neutrons from the No. 20 neutron beam line of China Spallation Neutron Source. A special detection method was applied to distinguish thermal neutrons from background radiation so that the planar detector was able to detect thermal neutrons. The intrinsic total detection efficiency of the detector for thermal neutrons was measured as 6.58%+/- 0.76%. The result demonstrates that planar CdZnTe detectors are also able to serve as thermal neutron detectors.

This study achieved high-performance depth-of-interaction (DOI) encoding in PET detectors through systematic optimization of crystal surface reflection processing, utilizing a light-sharing redirection mechanism. We employed a 23 x 23 LYSO array coupled to an 8 x 8 SiPM array under a 9:1 crystal-to-photodetector coupling ratio. Three detector designs with different surface treatments were investigated: Detector I (1.02 x 1.02 x 15 mm3 crystals, fully polished with E60 film), Detector II (1 x 1 x 15 mm3 crystals, ESR-wrapped with unpolished sidewalls), and Detector III (1 x 1 x 15 mm3 crystals, fully polished with ESR film). Detector I achieved the best DOI resolution (3.29 mm FWHM), while Detector II reached 4.13 mm, both detectors can achieve good crystal separation after layering, enabling effective discrete identification. Notably, the Detector III design enabled direct crystal discrimination without layered structures but exhibited compromised depth sensing capability. Energy resolution in the central region was 16.22%, 13.72%, and 11.54% for Detectors I-III, respectively, and the corresponding timing resolutions were 1.45 ns, 1.02 ns, and 1.64 ns. Taking all aspects into consideration, Detector II exhibited the most balanced performance, indicating that selective surface texturing (e.g., sidewall unpolishing) combined with high-reflectivity materials (ESR) can effectively optimize photon transport pathways. This methodology enables efficient DOI information capture while maintaining compatibility with existing PET electronics, providing a technically viable solution for next-generation high-resolution PET systems that integrates performance advantages with engineering feasibility.

Micro X-ray fluorescence (mu XRF) can obtain the elemental distribution in the surfaces of cultural relics and provide valuable information for archaeologists. We developed a surface adaptive mu XRF scanner enabling large-area elemental imaging on cultural relics with arbitrary irregular surfaces, overcoming limitations of existing systems constrained by object shape and size. We conducted mu XRF scanning on an enamel cylinder covered with paper, a fallen painting from ancient architecture and an unearthed gnomon shadow template, generating the corresponding elemental distribution images. Using the surface adaptive mu XRF scanner, we successfully conducted large-area mu XRF scanning of cultural relics with irregular surfaces, obtaining detailed elemental distribution images. With the method we proposed, archaeologists will be able to perform large-area XRF scanning imaging on relics with irregular surfaces, rather than being limited to flat-surface scans. This innovation significantly extends the application scope of mu XRF for large-area scanning and is poised to achieve more breakthroughs in archaeology.

In many engineering applications, multi-objective optimization problems can be reformulated as single-objective problems with multiple constraints to improve computational efficiency. This paper discusses the characteristics and challenges of RF accelerating structure optimizations and proposes an enhanced single-objective optimization strategy based on progressive exploration method to find the global optimal solution within a large solution space characterized by a continuous and confined distribution of feasible solutions. It begins from an arbitrary feasible solution and progressively slides and expands the solution space fragment along the distribution path of feasible solutions to rapidly explore the entire space. By incorporating a re-initialization mechanism to enhance swarm diversity and introducing penalty factors in place of constraints to increase the number of feasible solutions, the algorithm significantly improves its ability to escape local optima traps. The proposed algorithm is applied to optimize a DAA structure, yielding satisfactory results and convergence speed. These results highlight the method’s effectiveness and its potential applicability to other complex constrained optimization problems.