Energy Detection Research Articles

New channel to search for dark matter at Belle II

We propose a new “disappearing positron track” channel at Belle II to search for dark matter, in which a positron that is produced at the primary interaction vertex scatters with the electromagnetic calorimeter to produce dark matter particles. Such scatterings can occur via either annihilation with atomic electrons, or the bremsstrahlung process with target nuclei. The main backgrounds are due to photons and neutrons that are produced in the same scatterings and then escape detection. We require a large missing energy and further veto certain activities in the KLM detector to suppress such backgrounds. To illustrate the sensitivity of the new channel, we consider a new physics model where dark matter interacts with the standard model via a dark photon, which decays predominantly to dark matter; we find that our proposed channel can probe some currently unexplored parameter space, surpassing both the mono-photon channel at Belle II and the NA64 constraints.

Unsupervised Learning for Improved Gamma-Ray Spectrometry in Pixelated Cadmium Zinc Telluride (CZT) Detectors

Machine learning has been found to be ubiquitously useful across many industries, presenting an opportunity to improve radiation detection performance using data-driven algorithms. Improved detector resolution can aid in the detection, identification, and quantification of radionuclides. In this work, a novel, data-driven, unsupervised learning approach is developed to improve detector spectral characteristics by learning, and subsequently rejecting, poorly performing regions of the pixelated detector. Feature engineering is used to fit individual characteristic photo peaks to a Doniach lineshape with a linear background model. Then, principal component analysis is used to learn a lower-dimension latent space representation of each photo peak where the pixels are clustered, and subsequently ranked, based on the cluster mean distance to an optimal point. Pixels within the worst cluster(s) are rejected to improve the full-width at half-maximum (FWHM) by 10% to 15% (relative to the bulk detector) at 50% net efficiency when applied to training data obtained from measurements of a 100 μCi 154Eu source using a H3D M400i pixelated cadmium zinc telluride detector. These results compare well with, but do not outperform, a greedy algorithm that accumulates pixels in order of FWHM from lowest to highest used as a benchmark. In the future, this approach can be extended to include the detector energy and angular response. Finally, the model is applied to newly seen natural and enriched uranium spectra relevant for nuclear safeguards applications.

Prompt gamma timing for proton range verification with TlBr and TlCl as pure Cherenkov emitters

Objective. Prompt gamma timing (PGT) uses the detection time of prompt gammas emitted along the range of protons in proton radiotherapy to verify the position of the Bragg peak (BP). Cherenkov detectors offer the possibility of enhanced signal-to-noise ratio (SNR) due to the inherent physics of Cherenkov emission which enhances detection of high energy prompt gamma rays relative to other induced uncorrelated signals. In this work, the PGT technique was applied to 3 semiconductor material slabs that emit only Cherenkov light for use in a full scale system: a 3 × 3 × 20 mm3 TlBr, a 12 × 12 × 12 mm3 TlBr, and a 5 × 5 × 5 mm3 TlCl. Approach. A polymethyl methacrylate (PMMA) target was exposed to a 67.5 MeV, 0.5 nA proton beam and shifted in 3 mm increments at the Crocker nuclear laboratory (CNL) in Davis, CA, USA. A fast plastic scintillator coupled to a photomultiplier tube (PMT) provided the start reference for the proton time of flight. Time of flight (TOF) distributions were generated using this reference and the gamma-ray timestamp in the Cherenkov detector. Main results. The SNR of the proton correlated peaks relative to the background was 20, 29, and 30 for each of the three samples, respectively. The upper limit of the position resolutions with the TlCl sample were 2 mm, 3 mm, and 5 mm for 30k, 10k, and 5k detected events, respectively. The time distribution of events with respect to the reference reproduced with clarity the periodicity of the beam, implying a very high SNR of the Cherenkov crystals to detect prompt gammas. Background presence from the neutron-induced continuum, prompt gammas from deuterium, or positron activation were not observed. Material choice and crystal dimensions did not seem to affect significantly the outcome of the results. Significance. These results show the high SNR of the pure Cherenkov emitters TlBr and TlCl for the detection of prompt gammas in a proton beam with current of clinical significance and their potential for verifying the proton range. The accuracy in determining shifts of the BP was highly dependent on the number of events acquired, therefore, the performance of these detectors are expected to vary with different beam conditions such as current, pulse repetition, and proton bunch width.

Compact pixelated scintillator detector investigation for gamma ray detection

Objective. A scintillator is a luminescent material that converts high-energy photons into visible light and is widely used in medical imaging. Different scintillators are applied to gamma imaging in proton therapy and boron neutron capture therapy (BNCT). A pixelated scintillator is suitable for position measurement. The energy resolution, detection efficiency and position measurement of pixelated scintillators coupled to a SiPM were investigated via two kinds of detectors. The goal of this manuscript was to accurately measure the gamma source position through spectrum analysis in selection of energy windows for characteristic gamma lines. Approach. Scintillators were effectively manufactured and encapsulated before testing, especially for the easily deliquescent LaBr3. The compact front-end electronic prototype modules with 2 × 2 array SiPMs in stacked form and 1 × 10 array SiPMs in parallel form were developed for gamma ray energy resolution and efficiency measurements with a radioactive source of 22Na, which had two energy gamma lines at 511 keV and 1274 keV. Energy calibration was used for accurate energy window selection when measuring the position of the gamma source. Main results. Evident inconsistencies were present between different pixels of the same type of scintillator. Thus, an energy calibration method was needed. LaBr3 was the first candidate scintillator for the gamma ray spectrum measurement since it exhibited the best performance with an energy resolution of ∼5%. The recommended size of LaBr3 was 5 × 5 mm2, which had a higher efficiency than the 3 × 3 mm2 size. The gamma count of the multiple mode of the 2 × 2 array was much higher than that of the single mode, while the energy resolution was poorer. Thus, multiple mode was not suitable for gamma ray detection. The 1 × 10 array detector had the potential to measure the gamma ray source position and could be used for proton therapy and BNCT. A small deviation of 0.22 cm was observed in the measurement of the source center position with Energy Window 1 for 511 keV and Energy Window 2 for 1274 keV before the energy calibration. No deviation was observed after energy calibration. Thus, to achieve a higher accuracy position measurement, automatic energy calibration algorithm was coded into data acquisition software. Significance. The characteristic gamma lines produced by particle therapy are abundant and useful for imaging technology. Our developed compact pixelated scintillator detector coupled with SiPMs could measure the gamma spectrum with high resolution. The energy calibration and window selection method could measure the position of the source with high accuracy. Therefore, an advanced imaging device based on the energy spectrum for particle therapy could be potentially attainable.

ED-SS based Cognitive Radio (CR) over Various Fading Channels for Modern Wireless Communications

Cognitive Radio (CR) has already been proved to provide the better solution for the problem of spectrum insufficiency in which every wireless system occupies the limited bandwidth spectrum (LBS) allotting to the licensed users. Sometimes, this LBS is empty in a certain place at a certain time that is called as spectrum holes estimated by spectrum sensing (SS). The spectrum sensing is the main technique used in cognitive radio and various spectrum sensing techniques are available in the literature such as: energy detection (ED), matched filter detection (MFD) and cyclo-stationary feature detection (CFD). In the present work, energy detection-spectrum sensing (ED-SS) based cognitive radio system has been implemented which can analyze the energy of the spectrum. The various fading channels such as additive white gaussian noise (AWGN), Rayleigh, and Nakagami have been incorporated in the present work over which the performance of cognitive radio has been analyzed and compared using the ED-SS technique. The experimental results show thatt the detection of the presence of primary user signal using proposed ED-SS technique is easier with low computational complexities in Cognitive Radio (CR) networks. The present work also demonstrates when the performance of proposed ED-SS technique over aforesaid three fading channels are compared, Nakagami-m fading channels exhibits superior improvement in Pd compared to Rayleigh and AWGN fading channels

A Double-Threshold Cooperative Spectrum Sensing Algorithm in the Internet of Vehicles

To address the shortage of wireless spectrum resources caused by the rapid development of the Internet of Vehicles, spectrum sensing technology in cognitive radio is employed to tackle this issue. In pursuit of superior outcomes, a double-threshold cooperative spectrum sensing algorithm is introduced. This algorithm enhances traditional energy detection technology to mitigate the high sensitivity to noise interference in the Internet of Vehicles environment. A double-threshold judgment mechanism can be established based on the uncertainty of noise. Varying fusion rules are implemented in the collaborative spectrum sensing scheme according to the density of vehicles and the spectrum resource demand. Simulation results demonstrate that the performance of the double-threshold cooperative spectrum sensing algorithm surpasses that of the traditional single-threshold energy detection scheme, particularly evident under lower Signal-to-Noise Ratio (SNR) conditions. Moreover, the proposed algorithm exhibits superior sensing performance in environments characterized by higher noise uncertainty.

Pushing the high count rate limits of scintillation detectors for challenging neutron-capture experiments

One of the critical aspects for the accurate determination of neutron capture cross sections when combining time-of-flight and total energy detector techniques is the characterization and control of systematic uncertainties associated to the measuring devices. In this work we explore the most conspicuous effects associated to harsh count rate conditions: dead-time and pile-up effects. Both effects, when not properly treated, can lead to large systematic uncertainties and bias in the determination of neutron cross sections. In the majority of neutron capture measurements carried out at the CERN n_TOF facility, the detectors of choice are the C6D6 liquid-based either in form of large-volume cells or recently commissioned sTED detector array, consisting of much smaller-volume modules. To account for the aforementioned effects, we introduce a Monte Carlo model for these detectors mimicking harsh count rate conditions similar to those happening at the CERN n_TOF 20 m flight path vertical measuring station. The model parameters are extracted by comparison with the experimental data taken at the same facility during 2022 experimental campaign. We propose a novel methodology to consider both, dead-time and pile-up effects simultaneously for these fast detectors and check the applicability to experimental data from 197Au(n, γ), including the saturated 4.9 eV resonance which is an important component of normalization for neutron cross section measurements.

Enhancing spectrum utilization and primary user detection in cognitive radio networks through greedy cooperative spectrum sensing

The utilization of the spectrum is optimized through which primary users of modern wireless communication technologies might obtain a higher chance of detection. The research aims to study how the NI-USRP hardware platform can be used to set up greedy cooperative spectrum sensing for cognitive radio networks. Research primarily deals with energy detection and eigenvalue-based detection approaches, both of which are highly recognized for their capacity to sense the spectrum without having prior knowledge of the primary user signals. In the hardware arrangement, there is one transmitter and two cognitive radio receivers. LABVIEW makes it simple to deploy and maximizes the detection probability across a large sample. Here, it was demonstrated that cooperative spectrum sensing is superior to non-cooperative spectrum sensing, which results in a reduction in the risk of errors occurring during detection. The research discovered that the OR combination rule has a higher detection probability than the AND rule at the same time. The research emphasizes the significance of expanding cooperative spectrum sensing to improve overall detection capabilities. SNRs that are more than 10 dB allow the energy detector to operate, and the eigenvalue detector continues to work when the SNR drops to –9 dB.

The Applications of WFEM in the Exploration of Medium-Depth Geothermal Resources

Medium-depth geothermal energy mainly consists of two types, hydrothermal and dry-heat rock. With huge resource reserves, its exploration and development are of great significance to the transformation of China’s energy structure and the realization of the goal of “Double-Carbon”. From the differences in the electrical characteristics between hydrothermal and hot dry rock geothermal resources, this project uses the numerical modeling of the wide field electromagnetic (WFEM) method as a tool to design a model of typical geothermal resources. Then, we summarize and analyze the detection capability of the WFEM by calculating the WFEM response under the conditions of different depths, resistivity values, thickness-to-depth ratios and other parameters. Moreover, aiming at the key problems faced by the WFEM in real applications, we discuss the factors affecting the detection accuracy and give solutions to improve the reliability of the detection results. Finally, the medium-depth geothermal energy detection in the Jiyang Depression of Shandong is presented as an example, showing how the WFEM detection technique was used with the inversion result profile obtained. By comparing the results of geomagnetic and seismic detection on the same survey line, it is demonstrated that the WFEM method has great potential in the exploration of medium-depth geothermal energy and can be the preferred method.

Difference Processing Framework for Noncoherent MIMO With Energy or Differential Detection

Difference Processing Framework for Noncoherent MIMO With Energy or Differential Detection

A secure framework for effective workload resource management

An efficient and dynamic role-based access-control (RBAC) model is presented in this work which utilizes access-control for internet of things (IoT) nodes while minimizing storage and computational overhead. Also, for the identification of the malicious packets at the gateway server, a machine learning method has been presented. In addition, a framework for data management techniques in the IoT environment is designed to ensure efficient and secure storage, management, and processing of IoT data. The results have been evaluated by using the Montage and Cybershake workload in terms of energy consumption, processing time, detection accuracy and misclassification rate. The results show that the proposed secure framework for effective workload resource management (SFE-WRM) attains better performance in comparison to the reliable and energy‐efficient route selection (REERS) and FTA-WRM method. Also, by using the security method, the proposed method provides better security to the IoT nodes during the data aggregation and processing of the workload. The ultimate aim of this work is to provide a solution for the development of a secure and efficient IoT environment that can address critical security challenges and enable the widespread adoption of IoT devices and services.

Hybrid spectral CT system with clinical rapid kVp-switching x-ray tube and dual-layer detector for improved iodine quantification.

Spectral computed tomography (CT) is a powerful diagnostic tool offering quantitative material decomposition results that enhance clinical imaging by providing physiologic and functional insights. Iodine, a widely used contrast agent, improves visualization in various clinical contexts. However, accurately detecting low-concentration iodine presents challenges in spectral CT systems, particularly crucial for conditions like pancreatic cancer assessment. In this study, we present preliminary results from our hybrid spectral CT instrumentation which includes clinical-grade hardware (rapid kVp-switching x-ray tube, dual-layer detector). This combination expands spectral datasets from two to four channels, wherein we hypothesize improved quantification accuracy for low-dose and low-iodine concentration cases. We modulate the system duty cycle to evaluate its impact on quantification noise and bias. We evaluate iodine quantification performance by comparing two hybrid weighting strategies alongside rapid kVp-switching. This evaluation is performed with a polyamide phantom containing seven iodine inserts ranging from 0.5 to 20 mg/mL. In comparison to alternative methodologies, the maximum separation configuration, incorporating data from both the 80 kVp, low photon energy detector layer and the 140 kVp, high photon energy detector layer produces spectral images containing low quantitative noise and bias. This study presents initial evaluations on a hybrid spectral CT system, leveraging clinical hardware to demonstrate the potential for enhanced precision and sensitivity in spectral imaging. This research holds promise for advancing spectral CT imaging performance across diverse clinical scenarios.

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

AbstractUnmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVs operate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVs makes CSS inefficient and even difficult to be implemented. In view of this, a cognitive UAV network model consisting of a pair of UAVs which follows a circular flight trajectory to participate in CSS is proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman‐Pearson and Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented.

Evaluation of the Central Copper Contact Pin Effect in High-Energy Region in Gamma-ray Spectrometry

The detector must be modeled in the most accurate way when Monte Carlo simulation method is used for efficiency calculation in gamma-ray spectrometric studies. This study aims to investigate the effect of the copper contact pin inside the detector on the efficiency of the HPGe detector for high gamma-ray energies. Simulated efficiencies were determined for 6 different energies in the energy range of 1460.8 keV up to 2614.5 keV in point and cylindrical source geometry. According to the modeling using PHITS Monte Carlo simulation code, the presence of copper contact pin at high gamma-ray energies caused a decrease of up to 6% in detector efficiency. It was emphasized that this ignored parameter should be included in the modeling like all other geometric parameters used in detector modeling, by showing the effect on the efficiency.

First experimental time-of-flight-based proton radiography using low gain avalanche diodes

Objective. Ion computed tomography (iCT) is an imaging modality for the direct determination of the relative stopping power (RSP) distribution within a patient’s body. Usually, this is done by estimating the path and energy loss of ions traversing the scanned volume utilising a tracking system and a separate residual energy detector. This study, on the other hand, introduces the first experimental study of a novel iCT approach based on time-of-flight (TOF) measurements, the so-called Sandwich TOF-iCT concept, which in contrast to any other iCT systems, does not require a residual energy detector for the RSP determination. Approach. A small Sandwich TOF-iCT demonstrator was built based on low gain avalanche diodes (LGADs), which are 4D-tracking detectors that allow to simultaneously measure the particle position and time-of-arrival with a precision better than 100 μm and 100 ps, respectively. Using this demonstrator, the material and energy-dependent TOF was measured for several homogeneous PMMA slabs in order to calibrate the acquired TOF against the corresponding water equivalent thickness (WET). With this calibration, two proton radiographs (pRads) of a small aluminium stair phantom were recorded at MedAustron using 83 MeV and 100.4 MeV protons. Main results. Due to the simplified WET calibration models used in this very first experimental study of this novel approach, the difference between the measured and theoretical WET ranged between 37.09% and 51.12%. Nevertheless, the first TOF-based pRad was successfully recorded showing that LGADs are suitable detector candidates for Sandwich TOF-iCT. Significance. While the system parameters and WET estimation algorithms require further optimization, this work was an important first step to realize Sandwich TOF-iCT. Due to its compact and cost-efficient design, Sandwich TOF-iCT has the potential to make iCT more feasible and attractive for clinical application, which, eventually, could enhance the treatment planning quality.

Spectrum sensing using deep learning for proficient data transmission in wireless sensor networks for wireless communication

Cognitive radio has been recommended for enhancing wireless spectrum usage, and an important part of cognitive radio is spectrum sensing. Conventional spectrum sensing techniques rely on energy detection and feature extraction from a received signal at a specific position. The advancement of artificial intelligence and deep learning has provided a chance to increase spectrum sensing efficiency. The proposed investigation presented a hybrid model comprising CNN, BILSTM, and Transformer Networks (TN) for spectrum sensing. With the aid of Transformer networks, the proposed model improves sensing accuracy even for lower SNR signals, particularly at −20 dB. The results of the proposed technique show that CNN-BILSTM-TN enhances spectrum sensing in comparison to earlier models studied in this domain. The proposed approach outperforms prior methods in terms of F1 Score and has an improved probability of detection at −20 dB and a lower likelihood of missed detection. Analysis of ten modulation techniques' performance criteria, such as the Jaccard index, Cohen Kappa coefficient, and Matthew's correlation coefficient, demonstrates that the suggested method's spectrum sensing is better even at lower SNRs.

Diverse and Aligned Audio-to-Video Generation via Text-to-Video Model Adaptation

We consider the task of generating diverse and realistic videos guided by natural audio samples from a wide variety of semantic classes. For this task, the videos are required to be aligned both globally and temporally with the input audio: globally, the input audio is semantically associated with the entire output video, and temporally, each segment of the input audio is associated with a corresponding segment of that video. We utilize an existing text-conditioned video generation model and a pre-trained audio encoder model. The proposed method is based on a lightweight adaptor network, which learns to map the audio-based representation to the input representation expected by the text-to-video generation model. As such, it also enables video generation conditioned on text, audio, and, for the first time as far as we can ascertain, on both text and audio. We validate our method extensively on three datasets demonstrating significant semantic diversity of audio-video samples and further propose a novel evaluation metric (AV-Align) to assess the alignment of generated videos with input audio samples. AV-Align is based on the detection and comparison of energy peaks in both modalities. In comparison to recent state-of-the-art approaches, our method generates videos that are better aligned with the input sound, both with respect to content and temporal axis. We also show that videos produced by our method present higher visual quality and are more diverse. Code and samples are available at: https://pages.cs.huji.ac.il/adiyoss-lab/TempoTokens/.

Imaging of 3 bright terrestrial gamma-ray flashes by the atmosphere-space interactions monitor and their parent thunderstorms

The Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS) includes an instrument designed to geolocate Terrestrial Gamma-ray Flashes (TGF) produced by thunderstorms. It does so with a coded aperture system shadowing the pixelated Low Energy Detector of the Modular X- and Gamma-ray Sensor (MXGS). Additionally, it locates associated lightning flashes with the Modular Multispectral Imaging Array (MMIA). Here we present 3 bright TGFs with very similar duration, fluency and observation distance. The innovative imaging capabilities allow us to determine the TGF position and correlate the TGF-lightning parent event in time and position simultaneously. The accurate position determination and distance to the observer allow us to perform a comparative study of TGF characteristics. These TGFs were produced in association with lightning flashes below the highest cloud tops of developing to mature convective cells. In one event, GLM (Geostationary Lightning Mapper) cloud flash rates were slowing down after the TGF while negative cloud-to-ground flashes suddenly ceased from 10 to 5 min before the TGF. An 8-stroke (strongest: -93 kA) cloud-to-ground flash occurred 31 s before the TGF. Vertical profiles from the ERA5 reanalysis data suggest TGFs may be produced in variety of tropical environments.

Research on Power Load Data Acquisition and Integrated Transmission Systems in Electric Energy Calculation and Detection

This paper presents the crucial area of power load data acquisition with an integrated transmission system for precise calculation and detection of electric energy. With the advances in technology, management and optimization of energy has become critical for sustainability and economic reasons. Thus, we have targeted the cutting-edge methods for data gathering of power load along with its efficient transmission previously reviewed. We scrutinized the current methods and technologies used in power load data acquisition and identified their limitations along with areas of improvements. We followed advanced sensors and measuring devices for data collection employed an integrated transmission system with up-to-the-minute communication protocols and data processing algorithms. These were experimentally verified to improve the accuracy and reliability of the electric energy calculations. The real-world case studies were included for its practical implementations to provide an insight into its impacts. The results of this study provide a maturing outlook along with valuable analysis for electric energy calculation and detection. The system due to its potential for enhancing the energy management and efficiency can have a real-life and profound significance in sustainable and economic handling of the increasing load of energy.

Highest resolution high signal-to-noise ratio sonic slowness and application to Brothers Volcano, Kermadec Arc, New Zealand

High resolution and accurate acquisition of underground sonic properties is essential for energy detection and earth exploration. An effective method is developed to accurately calculate sonic slowness at the highest resolution (receiver spacing of 0.5 ft) with a high signal-to-noise ratio. The method uses supervirtual interferometry to reconstruct a large number of waveforms for slowness extraction using redundant information from overlapping interreceiver spacing. Synthetic calculations are used to verify the validity of the method, especially in the limited case of strong noise interference and the presence of damaged channels in the array receivers, to accurately calculate the formation of sonic slowness. The theory is applied to process and interpret low data-quality field measurements acquired at Brothers Volcano from the International Ocean Discovery Program. The processing analysis indicates that in the case of very weak formation signals, the residual tool waves from the wireline sonic logging can still interfere with the high-resolution processing, whereas this method can effectively suppress this interference and accurately obtain the formation sonic velocity.