In industrial manufacturing processes, the detection of defective products caused by unforeseen circumstances is crucial. Although unsupervised anomaly detection methods have been widely applied in this domain, these meth ods often suffer from frequency bias during training, leading to uneven learning of frequency information by the model and resulting in missed or false detections. To address this issue, this study proposes a Joint Decision Normalizing Flow based on Frequency Separation. Specifically, Class Attention in Image Transformer(CaiT) is employed to separate low-frequency and high-frequency features of normal samples, respectively, while Normalizing Flow is employed to estimate the distributions in different frequency domains. Furthermore, a Feature Fusion Attention mechanism is proposed to effectively integrate high and low-frequency information, ensuring a balanced representation of detailed and structural features. Finally, decisions are derived by synthe sizing the distribution information from these distinct frequency domains. This method mitigates frequency bias during training to enhance detection accuracy. Comprehensive experiments conducted on the MVTec AD dataset demonstrate that our method achieves an AUROC of 99.5 % for image-level anomaly detection, representing state-of-the-art performance with superior robustness. Additionally, an AUPRO of 98.2 % validates the method's effectiveness in detecting fine-grained anomalies.

The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R < 17.2 m) of the scintillator is required to be approximately 7 Hz for energies above 0.7 MeV, resulting in an accidental coincidence background of about 1 event per day for reactor neutrino physics analyses. Since the beginning of the construction phase, we have screened the natural radioactivity content of thousands of materials, to select those that meet the design background budget. The radioactive impurity concentrations of the materials ultimately used in the JUNO detector are summarized in this paper. The construction of the entire detector and the subsequent filling of the liquid scintillator were completed in August 2025. From the initial data, the total count rate of natural radioactivity within the detector's fiducial volume has met the requirements and is sufficient to support the reactor antineutrino analysis.

We present a data acquisition (DAQ) software based on the MIDAS framework, specifically for gaseous detectors to support the detector deployments and applications. It implements a comprehensive suite of functions, including parameter configuration, data acquisition, decoding, and storage, alongside web-based operation and real-time monitoring capabilities. We establish a fully unified workflow spanning data acquisition to offline analysis, enabling real-time visualization of signal waveforms and energy spectra. The system has been successfully deployed in the PandaX-III experiment, which utilized a high-pressure gaseous detector to search for neutrinoless double beta decay. Its performance and stability have been validated through tests involving two distinct electronics setups and joint commissioning with the detector.

The Jiangmen Underground Neutrino Observatory (JUNO) aims to determine the neutrino mass ordering. As a satellite experiment of JUNO, the Taishan Antineutrino Observatory (TAO) is designed to precisely measure the reactor antineutrino energy spectrum at a near site, providing essential reference data for JUNO. TAO represents a novel cryogenic liquid scintillator experiment, utilizing Silicon Photomultipliers (SiPMs) for photon detection and operating at -50 ^∘ C to suppress SiPM dark noise. A full-size prototype of the central detector of TAO (approximately 2 ^∘ C, and using the surface flux of ground cosmic muons in the warm-up phase of the detector. The detector simulation software of TAO was adapted to the configuration of the prototype and the Monte Carlo prediction shows good agreement with the Co-60 data. A mild variation of the detector response was observed after correcting the temperature effect of the SiPM performance with the cosmic muon data.

Muography is a non-invasive imaging technique that uses cosmic-ray muons, commonly divided into transmission (absorption) and scattering muography. For transmission muography, the inversion algorithm critically determines reconstruction quality. However, widely used schemes may produce smearing artifacts when measurement locations are limited and data are sparse. We develop an optimized Metropolis–Hastings (M–H) algorithm that mitigates smearing and retrieves sharper, more accurate density distributions without auxiliary data. Additionally, we implement an inverse distance weighting (IDW) approach to reconstruct the air–rock interface from muon measurements. The optimized M–H algorithm is applied in Monte Carlo simulations and applied to field data from the TianQin Tunnel experiment using the MuGrid-v2 detector. The IDW-reconstructed air–rock interface is validated against Light Detection and Ranging (LiDAR) measurements. In simulations, the optimized M–H algorithm improves high-density anomaly detection precision from 42% to 100% at threshold 5.1 g/cm^3, with gains of 6% to 42% across other threshold and low-density scenarios, together with the TianQin Tunnel reconstructions, these results demonstrate the effectiveness of the proposed approach.

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kton liquid scintillator-based, low-radioactivity, multi-purpose neutrino detector located 693 meters (1800 m.w.e.) underground in the Guangdong province, China. To detect scintillation light produced in the target, the detector is equipped with 17,612 20-inch photomultipliers (PMTs), forming the Large PMT system (LPMT). In addition, 25,600 3-inch photomultipliers (the Small Photomultiplier System or SPMT) are deployed in the gaps between the LPMTs. This paper presents the design and performance of the underwater front-end electronics developed for the SPMT system. It details the individual electronics boards and their key components, the inter-board interfaces, the system-level design, and the firmware architecture that supports data acquisition and control. It also outlines mechanical and thermal integration, board validation procedures, and system performance metrics. The readout chain includes digitization of 128 PMT channels per unit, synchronized time-stamping, charge measurement, event packaging, and bandwidth management. Comprehensive validation confirms the system's readiness to meet JUNO's stringent physics goals. The underwater electronics achieve noise levels as low as 0.04 photoelectrons with minimal crosstalk (below 0.4

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20-kiloton liquid scintillator neutrino detector, located 650 meters (1800 m.w.e.) underground in Jiangmen, Guangdong, China. JUNO is primarily designed for reactor neutrino measurements and has been taking data since 2025. With the largest mass of its kind and an excellent energy resolution, JUNO is a leading observatory for high-precision measurements of MeV neutrinos. The standard global trigger system serves as the primary trigger for JUNO. We present a newly developed multi-messenger trigger system that extends the capabilities of the global trigger by providing a lower energy threshold and an independent monitoring capability. During the 2025 operation, it achieved an effective energy threshold of approximately 110 +/- 10 keV, providing a lower threshold configuration suitable for low-energy event analysis. The system shows the potential to further reduce the threshold to well below 100 keV. Based on the multi-messenger trigger system, an astrophysical monitor has been developed to receive and process external alerts from other messengers, such as gravitational-wave observations. A Transient Neutrino Burst Monitor is integrated to detect short-time-scale neutrino burst events and enables real-time monitoring of transient astrophysical phenomena. The system is sensitive to neutrino bursts from core-collapse supernovae within a distance of about 250 kpc.

The spontaneous conversion of muonium to antimuonium is an interesting charged lepton flavor violation phenomenon that offers a sensitive probe for potential new physics and serves as a tool to constrain the parameter space beyond the Standard Model. The Muonium-to-Antimuonium Conversion Experiment (MACE) was designed to utilize a high-intensity muon beam, a Michel electron magnetic spectrometer, a positron transport system, and a positron detection system to either discover or constrain this rare process with a conversion probability of 𝒪(10^-13) . This article presents an overview of the theoretical framework and a detailed description of the experimental design for muonium-to-antimuonium conversion.

Over 25,600 3-inch photomultiplier tubes (PMTs) have been instrumented for the central detector of the Jiangmen Underground Neutrino Observatory. Each PMT is equipped with a high-voltage divider and a frontend cable with waterproof sealing. Groups of sixteen PMTs are connected to the underwater frontend readout electronics via specialized multi-channel waterproof connectors. This paper outlines the design and mass production processes for the high-voltage divider, the cable and connector, as well as the waterproof potting of the PMT bases. The results of the acceptance tests of all the integrated PMTs are also presented.

This paper presents an energy resolution study of the JUNO experiment, incorporating the latest knowledge acquired during the detector construction phase. The determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 MeV. To achieve this ambitious goal, significant efforts have been undertaken in the design and production of the key components of the JUNO detector. Various factors affecting the detection of inverse beta decay signals have an impact on the energy resolution, extending beyond the statistical fluctuations of the detected number of photons, such as the properties of the liquid scintillator, performance of photomultiplier tubes, and the energy reconstruction algorithm. To account for these effects, a full JUNO simulation and reconstruction approach is employed. This enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy resolution. The results of this study reveal an energy resolution of 2.95% at 1 MeV. Furthermore, this study assesses the contribution of major effects to the overall energy resolution budget. This analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data collection. Moreover, it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

Muography, traditionally recognized as a potent instrument for imaging the internal structure of gigantic objects, has initialized various interdisciplinary applications. As the financial and labor costs of muography detector development hinder their massive applications, we develop a novel muon detector called MuGrid by coupling a monolithic plastic scintillator with a light guide array in order to achieve competitive spatial resolution while substantially reducing production costs. For a prototype detector in 30 x 30 cm(2), the intrinsic spatial resolution has been optimized toward a millimeter scale. An outdoor field muography experiment was conducted to monitor two buildings for validation purposes. The test successfully resolved the geometric influence of architectural features based on the attenuation of muon flux in good agreement between experimental results and the simulation prediction.

Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by ( $$\alpha ,\,n$$ α , n ) reactions. In organic liquid scintillator detectors, $$\alpha $$ α particles emitted from intrinsic contaminants such as $$^{238}$$ 238 U, $$^{232}$$ 232 Th, and $$^{210}$$ 210 Pb/ $$^{210}$$ 210 Po, can be captured on $$^{13}$$ 13 C nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, ( $$\alpha ,\,n$$ α , n ) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable $$^{13}$$ 13 C $$(\alpha ,\,n)^{16}$$ ( α , n ) 16 O event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors.

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO's sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is planned by studying the fine interference pattern caused by quasi-vacuum oscillations in the oscillated antineutrino spectrum at a baseline of 52.5 km and is completely independent of the CP violating phase and neutrino mixing angle theta(23). The sensitivity is obtained through a joint analysis of JUNO and Taishan Antineutrino Observatory (TAO) detectors utilizing the best available knowledge to date about the location and overburden of the JUNO experimental site, local and global nuclear reactors, JUNO and TAO detector responses, expected event rates and spectra of signals and backgrounds, and systematic uncertainties of analysis inputs. We find that a 3 sigma median sensitivity to reject the wrong mass ordering hypothesis can be reached with an exposure of about 6.5 years x 26.6 GW thermal power.

Volatile organic compounds (VOCs), particularly xylene, are hazardous air pollutants that pose significant health risks due to their toxicity and widespread occurrence in indoor and industrial environments. In this work, we report a xylene gas sensor based on MoO3 decorated with SrMoO4 nanoparticles, designed to achieve enhanced sensing performance through heterojunction engineering. The SrMoO4/MoO3 composites were synthesized via a facile solvothermal route, enabling uniform dispersion of SrMoO4 on the MoO3 surface. Among the fabricated samples, the optimized SrMoO4/MoO3 sensor exhibited a high response of 29.2 toward 20 ppm xylene at 200 degrees C, with rapid response/recovery times (5/7 s), surpassing that of pristine MoO3 by 15.4 times. Moreover, the sensor also showed excellent selectivity toward xylene over other interfering gases such as acetone, trimethylamine, formaldehyde, and ammonia. The superior sensing behavior is attributed to the combined effects of n-n heterojunction formation, reduced crystallite size with lattice strain, increased surface area and pore volume, band gap narrowing, and enhanced chemisorbed oxygen and oxygen vacancy concentration. This study proposes an effective route for constructing advanced xylene sensor through rational heterojunction engineering.

As the multidisciplinary applications of cosmic-ray muons expand to large-scale and wide-area scenarios, the construction of cosmic-ray muon detector arrays has become a key solution to overcome the hardware limitations of individual detector. For muography, the array-based detector design enables fast-scanning of large target objects, allowing for rapid identification of density variation regions, which can improve the efficiency of tomography. This paper integrates scintillator detector technology with Internet of things (IoT) technology, proposing a novel array networking model for nationwide deployment. The model enables long-distance data collection and distribution, laying the foundation for future multidisciplinary applications such as muography and other fields.

Abstract We explore the decay of bound neutrons in the JUNO liquid scintillator detector into invisible particles (e.g., $$n\rightarrow 3 \nu $$ n → 3 ν or $$nn \rightarrow 2 \nu $$ n n → 2 ν ), which do not produce an observable signal. The invisible decay includes two decay modes: $$ n \rightarrow { inv} $$ n → inv and $$ nn \rightarrow { inv} $$ n n → inv . The invisible decays of s-shell neutrons in $$^{12}\textrm{C}$$ 12 C will leave a highly excited residual nucleus. Subsequently, some de-excitation modes of the excited residual nuclei can produce a time- and space-correlated triple coincidence signal in the JUNO detector. Based on a full Monte Carlo simulation informed with the latest available data, we estimate all backgrounds, including inverse beta decay events of the reactor antineutrino $${\bar{\nu }}_e$$ ν ¯ e , natural radioactivity, cosmogenic isotopes and neutral current interactions of atmospheric neutrinos. Pulse shape discrimination and multivariate analysis techniques are employed to further suppress backgrounds. With two years of exposure, JUNO is expected to give an order of magnitude improvement compared to the current best limits. After 10 years of data taking, the JUNO expected sensitivities at a 90% confidence level are $$\tau /B( n \rightarrow { inv} ) > 5.0 \times 10^{31} \, \textrm{years}$$ τ / B ( n → inv ) > 5.0 × 10 31 years and $$\tau /B( nn \rightarrow { inv} ) > 1.4 \times 10^{32} \, \textrm{years}$$ τ / B ( n n → inv ) > 1.4 × 10 32 years .

High-fidelity beamline models typically involve particle-tracking and particle-matter interaction, which are intensive computationally demanding and time-consuming. This has led researchers to adopt data-driven surrogate models as an alternative to complex physics simulations. Training a data-driven model requires many labeled data, prompting researchers to simulate diverse beamline settings and obtain corresponding labels. However, the required dataset grows increasingly large as the number of adjustable parameters increases. Therefore, this study proposes a data-efficient surrogate modeling method that employs a student-teacher framework combined with active learning (AL) query strategies to minimize labeled samples while ensuring model accuracy. The proposed method is evaluated on the energy selection system (ESS) design of the Huazhong University of Science and Technology proton therapy facility (HUST-PTF). The results show that: (i) Training the surrogate model using 684 labeled samples selected via query strategy achieves a relative error below 5% for 90% of the samples in the test set. (ii) Compared to the beamline model built by Beam Delivery Simulation (BDSIM), the computational efficiency of the surrogate model is enhanced by a factor of O(107).

Large-scale organic liquid scintillator detectors are highly efficient in the detection of MeV-scale electron antineutrinos. These signal events can be detected through inverse beta decay on protons, which produce a positron accompanied by a neutron. A noteworthy background for antineutrinos coming from nuclear power reactors and from the depths of the Earth (geoneutrinos) is generated by (alpha, n) reactions. In organic liquid scintillator detectors, alpha particles emitted from intrinsic contaminants such as U-238, Th-232, and Pb-210/Po-210, can be captured on C-13 nuclei, followed by the emission of a MeV-scale neutron. Three distinct interaction mechanisms can produce prompt energy depositions preceding the delayed neutron capture, leading to a pair of events correlated in space and time within the detector. Thus, (alpha, n) reactions represent an indistinguishable background in liquid scintillator-based antineutrino detectors, where their expected rate and energy spectrum are typically evaluated via Monte Carlo simulations. This work presents results from the open-source SaG4n software, used to calculate the expected energy depositions from the neutron and any associated de-excitation products. Also simulated is a detailed detector response to these interactions, using a dedicated Geant4-based simulation software from the JUNO experiment. An expected measurable C-13(alpha, n)O-16 event rate and reconstructed prompt energy spectrum with associated uncertainties, are presented in the context of JUNO, however, the methods and results are applicable and relevant to other organic liquid scintillator neutrino detectors.

The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m^2 of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeV γ from neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4

The physics potential of detecting B-8 solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of C-13 nuclei in the liquid-scintillator detectors and the {expected} low background level, B-8 solar neutrinos would be observable in the CC and NC interactions on C-13 for the first time. By virtue of optimized event selections and muon veto strategies, backgrounds from the accidental coincidence, muon-induced isotopes, and external backgrounds can be greatly suppressed. Excellent signal-to-background ratios can be achieved in the CC, NC and ES channels to guarantee the B-8 solar neutrino observation. From the sensitivity studies performed in this work, we show that JUNO, with ten years of data, can reach the {1 sigma} precision levels of 5%, 8% and 20% for the B-8 neutrino flux, sin(2)theta(12), and Delta m(21)(2), respectively. It would be unique and helpful to probe the details of both solar physics and neutrino physics. In addition, when combined with SNO, the world-best precision of 3% is expected for the B-8 neutrino flux measurement.