Real Detector Research Articles

Charge Diffusion and Repulsion in Semiconductor Detectors.

Semiconductor detectors for high-energy sensing (X/γ-rays) play a critical role in fields such as astronomy, particle physics, spectroscopy, medical imaging, and homeland security. The increasing need for precise detector characterization highlights the importance of developing advanced digital twins, which help optimize the design and performance of imaging systems. Current simulation frameworks primarily focus on modeling electron-hole pair dynamics within the semiconductor bulk after the photon absorption, leading to the current signals at the nearby electrodes. However, most simulations neglect charge diffusion and Coulomb repulsion, which spatially expand the charge cloud during propagation due to the high complexity they add to the physical models. Although these effects are relatively weak, their inclusion is essential for achieving a high-fidelity replication of real detector behavior. There are some existing methods that successfully incorporate these two phenomena with minimal computational cost, including those developed by Gatti in 1987 and by Benoit and Hamel in 2009. The present work evaluates these two approaches and proposes a novel Monte Carlo technique that offers higher accuracy in exchange for increased computational time. Our new method enables more realistic performance predictions while remaining within practical computational limits.

Review of real time 2D dosimetry in external radiotherapy: Advancements and techniques

The objective of this paper is to provide a comprehensive review of the advancements and techniques in real time two-dimensional (2D) dosimetry for external radiation therapy with emphasis in vivo dosimetry and patient specific quality assurance. External radiation therapy plays a crucial role in cancer treatment, delivering high-energy radiation beams to target tumours while minimizing damage to surrounding healthy tissues. Accurate dosimetry, as both the measurement of the dose and its delivered location, is paramount to ensure effective treatment outcomes and minimize potential side effects.The planned content of this paper encompasses a thorough examination of the advancements made in 2D dosimetry techniques, including solid state and electronic systems. The evolution from traditional passive dosimetry to modern real time detectors, such as portal imaging, has revolutionized the field, offering enhanced precision, efficiency, and convenience. This review will discuss the principles, advantages, and limitations of these systems, along with their practical implementation and calibration procedures.Furthermore, the paper will highlight novel technologies, such as luminescence coatings, for quality assurance (QA) and real-time dose verification during treatment. The use of innovative materials and designs in dosemeters, including high spatial resolution detectors and tissue-equivalent phantoms, will also be explored. Additionally, the incorporation of advanced data analysis techniques, such as machine/deep learning algorithms, for dose reconstruction and QA will be addressed.The review will also explore the application of real time 2D dosimetry in modern clinical and pre-clinical modalities, including intensity-modulated radiation therapy and volumetric modulated arc therapy, stereotactic radiosurgery, image-guided radiation therapy, particle therapy, adaptive radiotherapy, electron and proton ultra-high dose rate therapy and very high energy electrons.By providing an up-to-date overview of the state-of-the-art in real time 2D dosimetry in vivo dosimetry and patient specific quality assurance, this paper aims to inform and guide professionals in the field, facilitating the adoption of cutting-edge techniques and improving the accuracy and safety of external radiotherapy treatments.

Rapid likelihood free inference of compact binary coalescences using accelerated hardware

Abstract We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has ∼ 6 million trainable parameters with training times ≲ 24 h. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of ∼ 6 s.

Exploring charge sharing compensation using inter-pixel coincidence counters for photon counting detectors by deep-learning from local information

Photon counting detectors (PCDs) have well-acknowledged advantages in computed tomography (CT) imaging. However, charge sharing and other problems prevent PCDs from fully realizing the anticipated potential in diagnostic CT. PCDs with multi-energy inter-pixel coincidence counters (MEICC) have been proposed to provide particular information about charge sharing, thereby achieving lower Cramér-Rao Lower Bound (CRLB) than conventional PCDs when assessing its performance by estimating material thickness or virtual monochromatic attenuation integrals (VMAIs). This work explores charge sharing compensation using local spatial coincidence counter information for MEICC detectors through a deep-learning method.
Approach: By analyzing the impact of charge sharing on photon count detection, we designed our network with a focus on individual pixels. Employing MEICC data of patches centered on POIs as input, we utilized local information for effective charge sharing compensation. The output was VMAI at different energies to address real detector issues without knowledge of primary counts. To achieve data diversity, a fast and online data generation method was proposed to provide adequate training data. A new loss function was introduced to reduce bias for training with high-noise data. The proposed method was validated by Monte Carlo (MC) simulation data for MEICC detectors that were compared with conventional PCDs. 
Main-Results: For conventional data as a reference, networks trained on low-noise data yielded results with a minimal bias (about 0.7%) compared with > 3% for the polynomial fitting method. The results of networks trained on high-noise data exhibited a slightly increased bias (about 1.3%) but a significantly reduced standard deviation (STD) and normalized root mean square error (NRMSE). The simulation study of the MEICC detector demonstrated superior compared to the conventional detector across all the metrics. Specifically, for both networks trained on high-noise and low-noise data, their biases were reduced to about 1% and 0.6%, respectively. Meanwhile, the results from a MEICC detector were of about 10% lower noise than a conventional detector. Moreover, an ablation study showed that the additional loss function on bias was beneficial for training on high-noise data.
Significance: We demonstrated that a network-based method could utilize local information in PCDs effectively by patch-based learning to reduce the impact of charge sharing. MEICC detectors provide very valuable local spatial information by additional coincidence counters. Compared with MEICC detectors, conventional PCDs only have limited local spatial information for charge sharing compensation, resulting in higher bias and standard deviation in VMAI estimation with the same patch strategy.&#xD.

Machine Learning for Accurate Energy Analysis of Double Beta Decay Events

Point-contact germanium detectors are used to detect and analyze particles and their decay processes. This research focuses on the search for a phenomenon known as neutrinoless double beta decay, where two neutrons within a nucleus transform into two protons, emitting electrons in the process. Unlike standard double beta decay, no neutrinos are emitted, suggesting that the neutrino may act as its own antiparticle, effectively canceling itself out. Detecting this process would provide crucial insights into the nature of neutrino mass. During such an event, the detector registers a spike in energy due to the emitted electrons, producing a step-like pulse in the output channels. The energy of the event is determined by the difference in charge between the ‘top’ and ‘bottom’ steps of the pulse. However, accurately determining the energy is complicated by the presence of a resistor in the detector, which causes the top step to decay exponentially back to a baseline in preparation for the next event. This decay makes it challenging to determine the true energy of the pulse. My work this summer focused on developing machine learning models to reconstruct the step-like nature of pulses from their decayed versions recorded by the detector. This involved constructing deep neural networks trained on inputs (decayed pulses) and corresponding targets (the original step pulses) to learn to accurately reconstruct the target from the input. Initially, the networks were trained on simulated detector data. Once proficient in reconstruction, electronic noise was added to the input pulses to simulate real-world conditions, under which the models were trained to remove both electronic noise and decay. The models were trained to handle pulses with varying decay constants to improve their generalization ability. These neural networks can now be applied to real detector data for accurate energy analysis of double beta decay events.

Evaluation of the Reliability of a Standby Redundancy System Under Real Conditions

This paper presents a novel analysis of a standby redundancy system under real conditions. The study considers the presence of a real commutator and failure detector in the system. Through a comprehensive failure mode analysis, mathematical relationships between different module characteristics are established. The results of investigation provide valuable insights for manufacturers, allowing them to evaluate the Mean Time To Failure (MTTF) of the system during the design phase and make informed decisions regarding the selection of failure rates for the detector and commutator. Overall, this work contributes to the effective operation of standby redundancy systems in practical applications.

Hybrid Convolutional Neural Network with Whale Optimization Algorithm (HCNNWO) Based Plant Leaf Diseases Detection

Plant diseases appear to be posing a serious danger to the production and availability of food globally. The main factor affecting the quality and productivity of agricultural products is the health of the plants. In this paper, we describe a modified plant disease detection using deep convolutional neural networks in real time. By employing image processing techniques to enlarge the plant illness photos, the plant disease sets of data were initially produced. To recognise plant illnesses, a system called Convolutional Neural Network combined with Wolf Optimisation algorithm (CNN-WO) was used. Finally, the Whale Optimization algorithm (WO) is used to maximise and optimizes getting input. And it is given to CNN's learning rate for classification process. This paper presents an image segmentation and classification technique to automatically identify plant leaf diseases. The suggested strategy increased accuracy, sensitivity, precision, F1 measure, and specificity of plant disease detection. According to this study, HCNNWO real detectors have improved, which would require deep learning. It would be an effective method for determining plant illnesses and other diseases within plants. According to the evaluation report, the suggested method offers good reliability. To evaluate how well the suggested algorithm performs in comparison to cutting-edge techniques such as SVM, BPNN and CNN, experiments are conducted on datasets that are openly accessible

Waves in a forest: a random forest classifier to distinguish between gravitational waves and detector glitches

The LIGO-Virgo-KAGRA (LVK) network of gravitational-wave (GW) detectors have observed many tens of compact binary mergers to date. Transient, non-Gaussian noise excursions, known as ‘glitches’, can impact signal detection in various ways. They can imitate true signals as well as reduce the confidence of real signals. In this work, we introduce a novel statistical tool to distinguish astrophysical signals from glitches, using their inferred source parameter posterior distributions as a feature set. By modelling both simulated GW signals and real detector glitches with a gravitational waveform model, we obtain a diverse set of posteriors which are used to train a random forest classifier. We show that random forests can identify differences in the posterior distributions for signals and glitches, aggregating these differences to tell apart signals from common glitch types with high accuracy of over 93%. We conclude with a discussion on the regions of parameter space where the classifier is prone to making misclassifications, and the different ways of implementing this tool into LVK analysis pipelines.

Simulation of RPC responses in mini-Iron Calorimeter for cosmic ray muon studies

A Prototype of the Iron Calorimeter for the India-based Neutrino Observatory is currently running in Madurai, India. This consists of twenty large area single gap Resistive Plate Chambers (RPCs) of size, ∼ 185 cm × 175 cm sandwiched between 11 layers of iron plates of thickness 5.6 cm. The total size of the mini-Iron Calorimeter(mini-ICAL) is 4 m × 4 m × 1.2 m and having a weight of 85 tons is a (1/600) scaled-down version of the ICAL detector of INO [1]. The detector is magnetized using iron plates and two sets of copper coils, each coil has 18 turns. The central region of the mini-ICAL, where the RPCs are placed, has a nearly uniform magnetic field of 1.4 T, similar to the magnetic field in the final ICAL detector. This prototype serves as the basis for the design of the demonstrators, which closely mimics the characteristics of a future ICAL. This detector is built to test the final electronics in the fringe field of the magnet and also to develop the experience to construct the ICAL detector. The mini-ICAL has been operational since 2018 and collects cosmic muon data with different configurations of RPC, and electronics, which are continuously changing for various R&D efforts. Dedicated efforts are made in parallel to measure the momentum and azimuthal angle of μ+ and μ- independently, which could be an important input to the cosmic neutrino event generators. To improve the precision of those measurements a dedicated simulation effort was made, particularly in the digitisation of the RPC signal for a real detector simulation, where efficiency, noise rate, strip multiplicity, etc. were matched with the real data collected during runs at mini-ICAL.

Design and implementation of a virtual nuclear pulse signal generator

This paper presents a virtual nuclear pulse signal generator. The pulse generator can be used in specific scenarios such as calibration and maintenance of nuclear detection systems, validation of nuclear signal processing algorithms, and nuclear physics teaching experiments, as a substitute for real radioactive sources and detectors to generate nuclear pulse signals. This helps to avoid exposing experimental personnel to radiation hazards. The pulse generator is capable of generating specific nuclear pulse signals according to experimental requirements, and these signals has the same physical characteristics as real nuclear pulse signals, including amplitude distribution, time and amplitude randomness. In the pulse generator, we conducted theoretical analysis of the radioactive source and detector, and established a model library for both, to obtain crucial parameters of the nuclear pulse signals. The pulse generator is built on a hardware framework with field-programmable gate array (FPGA) as the core, utilizing these key parameters to generate signals with realistic physical characteristics of nuclear pulse signals, including amplitude and time characteristics. The counting rate of the output nuclear pulse signals can be adjusted by configuring the hardware parameters of the pulse generator.

Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

Abstract The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.

Effect of Humidity on the Mobilities of Small Ions in Ion Mobility Spectrometry.

Ions in the ion mobility spectrometry (IMS) are mostly hydrated. A single peak in the drift time spectrum is usually generated by a mixture of ions differing in the number of attached water molecules. Under real IMS detector operating conditions, ions change their composition during movement in the drift region due to the changes in the number of water molecules attached to the ion. The impact of water vapor on the drift times of small ions at different temperatures was studied experimentally using an ion mobility spectrometer. The experiments were carried out for hydronium, ammonium, oxygen, chloride, bromide, and iodide ions. A theoretical model was developed, allowing us to calculate the effective mobility of ions for a given concentration of water vapor and temperature. The basic assumption adopted in this model was the linear dependence of the effective mobility coefficient on the mobility of ions with a certain degree of hydration. The weighting factors in this relationship are the abundances of individual types of ions. These parameters were determined by calculations based on the thermodynamics of the formation and disintegration of ionic clusters. From the known values of temperature, pressure, and humidity, the values of effective mobilities can be predicted quite accurately. The dependencies of reduced mobilities on the average degree of hydration were also determined. For these dependencies, the measurement points on the graphs are gathered along specific lines. This means that the average degree of hydration unambiguously determines the value of reduced mobility for a given type of ions.

Identifying Common and Idiosyncratic Explosive Behaviors in the Large Dimensional Factor Model with an Application to U.S. State-Level House Prices

Abstract This study applies the date-stamping methodologies for explosive behaviors proposed in the seminal work of Phillips, P. C. B., and J. Yu. (2011. “Dating the Timeline of Financial Bubbles during the Subprime Crisis.” Quantitative Economics 2 (3): 455–91), Phillips, P. C. B., S. Shi, and J. Yu. (2015a. “Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500.” International Economic Review 56 (4): 1043–78), and Phillips, P. C. B., S. Shi, and J. Yu. (2015b. “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors.” International Economic Review 56 (4): 1079–134) to a large dimensional factor model. To this end, we compare two methods of identifying common and idiosyncratic components: the Panel Analysis of Nonstationarity in Idiosyncratic and Common Components (PANIC) method by Bai, J., and S. Ng. (2004. “A Panic Attack on Unit Roots and Cointegration.” Econometrica 72 (4): 1127–77) and the Cross-Sectional regression (CS) method by Yamamoto, Y., and T. Horie. (2022. “A Cross-Sectional Method for Right-Tailed PANIC Tests under a Moderately Local to Unity Framework.” Econometric Theory (forthcoming)). We show that, when the explosive behavior lies only in the common component, the origination and termination dates are precisely estimated by either method. However, when the explosive behaviors exist in idiosyncratic components, only the CS method can detect them. We apply our method to the U.S. state-level real house price indices. We find that the 2000s boom was driven by not only the national bubble factors but also local components, while the 2010s onward expansion is dominated by the effect of national components.

A Modular Architecture of Command-and-Control Software in Multi-Sensor Systems Devoted to Public Security

Preventing terrorist attacks at soft targets has become a priority for our society. The realization of sensor systems for automatic threat detection in crowded spaces, such as airports and metro stations, is challenged by the limited sensing coverage capability of the devices in place due to the variety of dangerous materials, to the scanning rate of the devices, and to the detection area covered. In this context, effectiveness of the physical configuration of the system based on the detectors used, the coordination of the sensor data collection, and the real time data analysis for threat identification and localization to enable timely reactions by the security guards are essential requirements for such integrated sensor-based applications. This paper describes a modular distributed architecture of a command-and-control software, which is independent from the specific detectors and where sensor data fusion is supported by two intelligent video systems. Furthermore, the system installation can be replicated at different locations of a public space. Person tracking and later re-identification in a separate area, and tracking hand-over between different video components, provide the command-and-control with localization information of threats to timely activate alarm management and support the activity of subsequent detectors. The architecture has been implemented for the NATO-funded DEXTER program and has been successfully tested in a big city trial at a metro station in Rome both when integrated with two real detectors of weapons and explosives and as a stand-alone system. The discussion focuses on the software functions of the command-and-control and on the flexibility and re-use of the system in wider settings.

Auto-Focusing in Digital Holographic Microscopy Using Differential Cosine Score

Accurately estimating the reconstruction distance of samples is very important for digital holographic microscopy, various auto-focusing algorithms have been proposed. These algorithms determine focus position by finding the global peak or valley of the evaluation curve. However, the discrete step size is small in precise autofocus process, which causes that the function values around peak or valley are not much different. As a result, the sensitivity of the peak and valley values of the evaluation curve is low, and the focusing accuracy is also susceptible to noise. In this paper, an autofocusing method using differential cosine score (DCS) is proposed to improve the accuracy and sensitivity of evaluation curve. In this method, two virtual detectors are placed on both sides of the real detector at the same distance. Two ordinary focusing curves and one DCS curve can be obtained by using the cosine score function. Therefore, the focal plane can be automatically identified by searching the zero-crossing of DCS curve, which avoids the problem of low sensitivity of peak and valley values by traditional methods. We illustrate the validity of the proposed method with measurements on USAF resolution target, blood smear and Hela cell.

Architectural optimization and feature learning for high-dimensional time series datasets

As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study a problem in which the goal is to predict the presence of transient noise artifacts in a gravitational-wave detector from a rich collection of measurements from the detector and its environment. We argue that feature learning---in which relevant features are optimized from data---is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it can improve performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be impossible to discover. In our motivating application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies. Learning in such a high-dimensional setting is challenging. Through experiments with a variety of architectures, we identify two key factors in high-performing models: sparsity, for selecting relevant variables within the high-dimensional observations, and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through a systematic series of experiments on real gravitational-wave detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to a wide variety of problem settings with similarly high-dimensional, noisy, or partly irrelevant data.

Deep neural networks for estimation of temperature values for thermal ageing evaluation of nuclear power plant equipment

The average age of the world's nuclear power plants in operation today is over 30 years, which will force big decisions in the next decade related to the continuity of operation of these plants. If most of these plants are shut down after 40 years, it could place considerable stress on electricity systems, and push the Paris treaty's low-carbon energy production targets by 2050 even further out of reach. When NPPs are reaching the end of its lifetime, a license renewal is required. In the license renewal process it is necessary to present a set of documents, one of which is an environmental qualification program of electrical equipment, which includes a survey of the ageing management of all structures, systems and components, mainly those related to safety equipment. The main ageing affects are changes in physical properties due to the temperature, radiation, humidity, etc. Received by structures, systems and components since their operation starts, but these data may not be available, for various reasons, and the ageing studies will be hampered. In this article is proposed a methodology based on Deep Neural Network in order to simulate virtual temperature sensors, using as parameters measurements of real plant detectors in order to support ageing evaluation. For implementation and testing of the proposed approach, data from Brazilian pressurized water reactor were used. The results of the experiments described here with the proposed methodology show a promising path for solving this problem.

Characterisation and Quenching Correction for an Al2O3:C Optical Fibre Real Time System in Therapeutic Proton, Helium, and Carbon-Charged Beams.

Real time radioluminescence fibre-based detectors were investigated for application in proton, helium, and carbon therapy dosimetry. The Al2O3:C probes are made of one single crystal (1 mm) and two droplets of micro powder in two sizes (38 μm and 4 μm) mixed with a water-equivalent binder. The fibres were irradiated behind different thicknesses of solid slabs, and the Bragg curves presented a quenching effect attributed to the nonlinear response of the radioluminescence (RL) signal as a function of linear energy transfer (LET). Experimental data and Monte Carlo simulations were utilised to acquire a quenching correction method, adapted from Birks' formulation, to restore the linear dose-response for particle therapy beams. The method for quenching correction was applied and yielded the best results for the '4 μm' optical fibre probe, with an agreement at the Bragg peak of 1.4% (160 MeV), and 1.5% (230 MeV) for proton-charged particles; 2.4% (150 MeV/u) for helium-charged particles and of 4.8% (290 MeV/u) and 2.9% (400 MeV/u) for the carbon-charged particles. The most substantial deviations for the '4 μm' optical fibre probe were found at the falloff regions, with ~3% (protons), ~5% (helium) and 6% (carbon).

Deep representation of imbalanced spatio‐temporal traffic flow data for traffic accident detection

AbstractAutomatic detection of traffic accidents has a crucial effect on improving transportation, public safety, and path planning. Many lives can be saved by the consequent decrease in the time between when the accidents occur and when rescue teams are dispatched, and much travelling time can be saved by notifying drivers to select alternative routes. This problem is challenging mainly because of the rareness of accidents and spatial heterogeneity of the environment. This paper studies deep representation of loop detector data using long‐short term memory (LSTM) network for automatic detection of freeway accidents. The LSTM‐based framework increases class separability in the encoded feature space while reducing the dimension of data. The experiments on real accident and loop detector data collected from the Twin Cities Metro freeways of Minnesota demonstrate that deep representation of traffic flow data using LSTM network has the potential to detect freeway accidents in less than 18 min with a true positive rate of 0.71 and a false positive rate of 0.25 which outperforms other competing methods in the same arrangement.

MiniSPD Stand for Testing Si-Detectors

SPD (Spin Physics Detector) collaboration proposes to install a universal setup in the second interaction point of the NICA collider under construction (JINR, Dubna) to study the spin structure of the proton and deuteron. It plans to carry out research of spin-related phenomena with polarized proton and deuteron beams at a collision energy up to 27 GeV and a luminosity up to 1032 cm−2 s−1. MiniSPD stand is manufactured as a setup for testing SPD detector prototypes with cosmic muons at LHEP. It allows to carry out checkout of the Data Acquisition System (DAQ), the Detector Control System (DCS). Young physicists and students working at this test bench gain experience of work with real detectors of the future SPD setup. In this report, we give some information about the basic tasks of SPD projects. The results of simulation and comparison with data on cosmic rays at this stand for three modules of silicon plates are also presented.