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At least 829 records · Page 46

Accurate simulation of direct laser acceleration in a laser wakefield accelerator

In a laser wakefield accelerator (LWFA), an intense laser pulse excites a plasma wave that traps and accelerates electrons to relativistic energies. When the pulse overlaps the accelerated electrons, it can enhance the energy gain through direct laser acceleration (DLA) by resonantly driving the betatron oscillations of the electrons in the plasma wave. The traditional particle-in-cell (PIC) algorithm, although often the tool of choice to study DLA, contains inherent errors due to numerical dispersion and the time staggering of the electric and magnetic fields. Furthermore, conventional PIC implementations cannot reliably disentangle the fields of the plasma wave and laser pulse, which obscures interpretation of the dominant acceleration mechanism. Here, a customized field solver that reduces errors from both numerical dispersion and time staggering is used in conjunction with a field decomposition into azimuthal modes to perform PIC simulations of DLA in an LWFA. Comparisons with traditional PIC methods, model equations, and experimental data show improved accuracy with the customized solver and convergence with an order-of-magnitude fewer cells. Furthermore, the azimuthal-mode decomposition reveals that the most energetic electrons receive comparable energy from DLA and LWFA.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Wehnelt photoemission in an ultrafast electron microscope: Stability and usability

We tested and compared the stability and usability of three different cathode materials and configurations in a thermionic-based ultrafast electron microscope: (1) on-axis thermionic and photoemission from a custom 100 μm diameter LaB 6 source with a graphite guard ring, (2) off-axis photoemission from the Ni aperture surface of the Wehnelt electrode, and (3) on-axis thermionic and photoemission from a custom 200 μm diameter polycrystalline Ta source. For each cathode type and configuration, including the Ni Wehnelt aperture, we illustrate how the photoelectron beam-current stability is deleteriously impacted by simultaneous cooling of the source following thermionic heating. Furthermore, we demonstrate usability via collection of parallel- and convergent-beam electron diffraction patterns and by formation of the optimum probe size. We find that usability of the off-axis Ni Wehnelt-aperture photoemission is at least comparable to on-axis LaB 6 thermionic emission, as well as to on-axis photoemission [the heretofore conventional approach to ultrafast electron microscopy (UEM) in thermionic-based instruments]. However, the stability and achievable beam currents for off-axis photoemission from the Wehnelt aperture were superior to that of the other cathode types and configurations, regardless of the electron-emission mechanism. Beam-current stability for this configuration was found to be ±1% (one standard deviation from the mean) for 70 min (longest duration tested), and steady-state beam current was reached within the sampling-time resolution used here (~1 s) for 15 pA beam currents (i.e., 460 electrons per packet for a 200 kHz repetition rate). Repeatability and robustness of the steady-state condition were also found to be within ±1% of the mean. We discuss the implications of these findings for UEM imaging and diffraction experiments, for pulsed-beam damage measurements, and for practical switching between optimum conventional TEM and UEM operation within the same instrument.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Proton radiation effects in indium oxide using cascade molecular dynamics simulations

Metal oxide (MO) semiconductors, characterized by their wide band gaps and notable charge transport properties, are promising candidates for electronic applications in extreme environments, including near-Earth space. However, atomistic simulations of radiation–matter interactions in MOs remain challenging due to the limitations of existing interatomic potentials, which often fail to capture both the short-range repulsive forces essential for radiation damage modeling and the long-range electrostatic effects governing defect evolution. In this work, we develop a customized interatomic potential tailored for radiation damage simulations in indium oxide (In 2 O 3 ) as a model system, a representative MO material. Our potential integrates the Ziegler-Biersack-Littmark potential to accurately describe short-range interactions with Buckingham and Coulombic potentials to account for long-range forces. We perform molecular dynamics simulations of low-energy proton irradiation using this custom potential. We employ the primary knock-on atom (PKA) cascade method to study atomic displacements and primary defect formation. Simulations were conducted for 1 keV proton irradiation in four randomly chosen directions, and PKA-driven defect analyses at 5, 10, and 15 keV to examine the effects of direction and energy level on damage generation. Our results provide insight into the impact of irradiation direction and energy level on the cascade evolution and defect formation mechanisms.

Atomistic simulations↗

Mathematical solutions in internal dose assessment: A comparison of Python-based differential equation solvers in biokinetic modeling

Abstract In biokinetic modeling systems employed for radiation protection, biological retention and excretion have been modeled as a series of discretized compartments representing the organs and tissues of the human body. Fractional retention and excretion in these organ and tissue systems have been mathematically governed by a series of coupled first-order ordinary differential equations (ODEs). The coupled ODE systems comprising the biokinetic models are usually stiff due to the severe difference between rapid and slow transfers between compartments. In this study, the capabilities of solving a complex coupled system of ODEs for biokinetic modeling were evaluated by comparing different Python programming language solvers and solving methods with the motivation of establishing a framework that enables multi-level analysis. The stability of the solvers was analyzed to select the best performers for solving the biokinetic problems. A Python-based linear algebraic method was also explored to examine how the numerical methods deviated from an analytical or semi-analytical method. Results demonstrated that customized implicit methods resulted in an enhanced stable solution for the inhaled 60 Co (Type M) and 131 I (Type F) exposure scenarios for the inhalation pathway of the International Commission on Radiological Protection (ICRP) Publication 130 Human Respiratory Tract Model (HRTM). The customized implementation of the Python-based implicit solvers resulted in approximately consistent solutions with the Python-based matrix exponential method ( expm ). The differences generally observed between the implicit solvers and expm are attributable to numerical precision and the order of numerical approximation of the numerical solvers. This study provides the first analysis of a list of Python ODE solvers and methods by comparing their usage for solving biokinetic models using the ICRP Publication 130 HRTM and provides a framework for the selection of the most appropriate ODE solvers and methods in Python language to implement for modeling the distribution of internal radioactivity.

61 RADIATION PROTECTION AND DOSIMETRY↗

Hardware, methodology and applications of 2 + D backscatter Mössbauer spectroscopy with simultaneous x-ray and γ-ray detection

Abstract A unique method is presented for the acquisition and analysis of 57 Fe backscatter Mössbauer spectra with simultaneous detection of the resonant 14.4 keV γ -rays and the characteristic 6.4 keV x-rays, using a custom-built multi-parameter analyser constructed on the basis of commercial analogue to digital converters and high-speed digital latches. The system allows for the simultaneous registration of Doppler-modulation velocities and photon energies, with up to 4096 and 8192 digital channels respectively. This arrangement is in contrast to most related systems, which detect at a single narrow energy window per detector. Samples of arbitrary atomic structure, morphology and surface topography can be studied without altering the setup or the analysis procedure, provided that the samples are at least micrometre sized. The hardware and software that are used to acquire data with minimal dead time are described and the custom and self-contained methods for post-measurement energy discrimination, background correction and velocity-axis folding are discussed. The data are fit using a general Hamiltonian model for the nuclear energy levels of 57 Fe and a quantum mechanical description of the angular momentum coupling is utilised, with consideration of the crystalline and chemical disorder of the sample under examination. Three examples of distinct magnetic systems, with thicknesses ranging from 5 μ m to 6 mm, that were studied using this method are presented, these are: an amorphous CoFeB-based ribbon with ultra-soft coercivity for high-frequency applications, magnetically hard Nd-Fe-B thick films on Si substrates, examined in both as-deposited and annealed states, and a sample from the nickel-rich iron meteorite NWA 6259 that contains the atomically ordered, elevated coercivity, L 1 0 phase of FeNi, tetrataenite. The wide applicability and usefulness of this method is thus demonstrated on three distinct sample morphologies that required little to no surface preparation prior to examination.

Engineering↗

Design improvements to cables and connectors in the Majorana Demonstrator

The Majorana Demonstrator is an experiment constructed to search for neutrinoless double-beta decays in germanium-76 and to demonstrate the feasibility to deploy a ton-scale experiment in a phased and modular fashion. It consists of two modular arrays of natural and 76 Ge-enriched germanium p-type point contact detectors totaling 44.1 kg, located at the 4850 level of the Sanford Underground Research Facility in Lead, South Dakota, USA. The Demonstrator uses custom high voltage cables to bias the detectors, as well as custom signal cables and connectors to read out the charge deposited at the point contact of each detector. These low-mass cables and connectors must meet stringent radio purity requirements while being subjected to thermal and mechanical stress. A number of issues have been identified with the currently installed cables and connectors. An improved set of cables and connectors for the Majorana Demonstrator are being developed with the aim of increasing their overall reliability and connectivity. In conclusion, we will discuss some of the issues encountered with the current cables and connectors as well as our improved designs and their initial performance.

42 ENGINEERING↗

Production and testing of the powerboard for ATLAS ITk Strip barrel modules

The upgrade of the ATLAS ITk strips detector for HL-LHC will employ a custom PCB (powerboard) for on-module DC-DC conversion, HV switching, and monitoring. Production of about 14100 of such PCBs with high reliability is a big challenge. In this paper we will present the production procedure and quality control (QC) testing system for the powerboards to be installed on ITk Strip barrel modules. During production, about 150 powerboards will be produced every week. Each powerboard will need to pass QC tests before they are assembled to module. The QC tests include thermal cycling between -35 °C and 40 °C, soak test (continuous running of DC-DC converter at high load at 20 °C), and basic functionality and characterization electrical tests of the low voltage (LV), high voltage (HV), and monitoring functions of the powerboard. A custom QC test system is described, which is based on a reusable PCB (active board) with all testing circuits, a one-time powerboard carrier card, and a Z-turn SoC board which hosts and runs the testing program. The production procedure and QC test system has been tested with 400 powerboards during pre-production, and the system has been proven ready for production with the desired testing program, speed, safety, and cost. Performance and typical failures of powerboards observed during the QC test of pre-production powerboards is also presented.

47 OTHER INSTRUMENTATION↗

The COLUTA ADC ASIC and the ATLAS HL-LHC Liquid Argon Front-End Readout

Abstract The upgraded High-Luminosity Large Hadron Collider (HL-LHC) requires a new radiation tolerant ATLAS Liquid Argon (LAr) Calorimeter readout operating at 40 MHz with 16-bit dynamic range. The Front-End Board 2 (FEB2) will provide the front-end readout for the calorimeter, and will interface a series of custom ASICs due to radiation tolerance requirements of the HL-LHC. The COLUTA is a 65 nm CMOS custom 8-channel 15-bit 40 MSPS ADC ASIC developed for this application. This contribution will present performance results of the final prototype of the COLUTA, surpassing specifications at >11.5 ENOB for relevant frequencies, as well as non-linearity and radiation requirements. Preliminary integration results of the front-end readout components will also be described by means of the FEB2 Pre-Prototype, the “Slice Testboard”.

Instruments & Instrumentation↗

Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of $\mathcal{O}$(40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector's data volume by 57.1%–75.7%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 μW with an area of less than 0.2 mm 2 . The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗

Moving Beyond Mortality: Development and Application of a Desirability of Outcome Ranking (DOOR) Endpoint for Hospital-Acquired Bacterial Pneumonia and Ventilator-Associated Bacterial Pneumonia

Abstract Background Hospital-acquired and ventilator-associated bacterial pneumonia (HABP/VABP) are frequently caused by multidrug-resistant organisms. Patient-centered endpoints in clinical trials are needed to develop new antibiotics for HABP/VABP. Desirability of outcome ranking (DOOR) is a paradigm for the design, analysis, and interpretation of clinical trials based on a patient-centered, benefit-risk evaluation. Methods A multidisciplinary committee created an infectious diseases DOOR endpoint customized for HABP/VABP, incorporating infectious complications, serious adverse events, and mortality. We applied this to 2 previously completed, large randomized controlled trials for HABP/VABP. ZEPHyR compared vancomycin to linezolid and VITAL compared linezolid to tedizolid. For each trial, we evaluated the DOOR distribution and probability, including DOOR component and partial credit analyses. We also applied DOOR in subgroup analyses. Results In both trials, the HABP/VABP DOOR demonstrated similar overall clinical outcomes between treatment groups. In ZEPHyR, the probability that a participant treated with linezolid would have a more desirable outcome than a participant treated with vancomycin was 50.2% (95% confidence interval [CI], 45.1%­−55.3%). In VITAL, the probability that a participant treated with tedizolid would have a more desirable outcome than a participant treated with linezolid was 48.7% (95% CI, 44.8%–52.6%). The DOOR component analysis revealed that participants treated with tedizolid had a less desirable outcome than those treated with linezolid when considering clinical response alone. However, participants with decreased renal function had improved overall outcomes with tedizolid. Conclusions The HABP/VABP DOOR provided more granular information about clinical outcomes than is typically presented in clinical trials. HABP/VABP trials would benefit from prospectively using DOOR.

Immunology↗

Default Effects And Follow-On Behaviour: Evidence From An Electricity Pricing Program

We study default effects in the context of a residential electricity-pricing program. In the large-scale randomized controlled trial we analyse, one treatment group was given the option to opt-in to time-varying pricing while another was defaulted into the program but allowed to opt-out. We provide dramatic evidence of a default effect on program participation, consistent with previous research. A novel feature of our study is that we also observe how the default manipulation impacts customers’ subsequent electricity consumption. Passive consumers who did not opt-out but would not have opted in-comprising more than 70$\%$ of the sample-nonetheless reduce consumption in response to higher prices. Observing of this follow-on behaviour enables us to assess competing explanations for the default effect. We draw conclusions about the likely welfare effects of defaulting customers onto time-varying pricing.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Controller Hardware-in-the-Loop Modeling and Operation of a High-Power DC Charging Hub

High-power charging (HPC) concept will be a critical enabler for the wide-scale adoption and integration of electric vehicles (EVs). HPC hubs will have a significant impact on achieving this goal, particularly when considering the charging requirements of next-generation medium and heavy-duty vehicles (MD/HD) used in various vocational scenarios. This emphasizes the significance of evaluating and confirming the technical planning and multi-level operation of HPC hubs through modeling, protocol implementation, and simulation before hardware demonstration. The focus of this study is to develop the architecture, modeling, communication, and real-time controller hardware-in-the-loop (C-HIL) simulation of HPC hubs using the DC distribution system approach. Specifically, this work demonstrates the practical implementation of a custom Smart Energy Management System (SEMS) for a real-time C-HIL HPC hub simulation using the Open Charge Point Protocol (OCPP) for communication. OCPP is implemented on the Node-Red framework and the custom controller is executed in Python. We developed a rule-based controller to demonstrate the proposed approach.

ADVANCED PROPULSION SYSTEMS↗

Composable Programming of Hybrid Workflows for Quantum Simulation

We present a composable design scheme for the development of hybrid quantum/classical algorithms and workflows for applications of quantum simulation. Our object-oriented approach is based on constructing an expressive set of common data structures and methods that enable programming of a broad variety of complex hybrid quantum simulation applications. The abstract core of our scheme is distilled from the analysis of the current quantum simulation algorithms. Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design. We implement our design scheme using the hardware-agnostic programming language QCOR into the QuaSiMo library. To validate our implementation, we test and show its utility on commercial quantum processors from IBM, running some prototypical quantum simulations.

97 MATHEMATICS AND COMPUTING↗

SPATIOTEMPORAL TRACKING OF WIDE AREA POWER OUTAGE FROM NIGHT-TIME LIGHT IMAGERY

Monitoring progress of power restoration following extreme events is essential for situational awareness about spatio-temporal distribution of populations without power and to help with response efforts. Because of the proprietary nature of restoration data, and the difficulty in obtaining power outage data from utility companies in near real-time (e.g., during hurricane Maria (2017)), this project used satellite derived nighttime lights data from Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) to (i) monitor wide area power outage and (ii) estimate impacted customers over time to assist with restoration. A discussion of the methodology and its implementation during hurricanes Maria (2017) and Eta (2020) is presented in this paper. Future work will focus on calibrating estimated customers based on light intensity and density distribution, and generation of restoration profiles for emergency response.

Kar, Bandana↗

Cross-Market Price Difference Forecast Using Deep Learning for Electricity Markets

Price forecasting is in the center of decision making in electricity markets. Many researches have been done in forecasting energy prices while little research has been reported on forecasting price difference between day-ahead and realtime markets due to its high volatility, which however plays a critical role in virtual trading. To this end, this paper takes the first attempt to employ novel deep learning architecture with Bidirectional Long-Short Term Memory (LSTM) units to forecast the price difference between day-ahead and real-time markets for the same node. The raw data is collected from PJM market, processed and fed into the proposed network. The Root Mean Squared Error (RMSE) and customized performance metric are used to evaluate the performance of the proposed method. Case studies show that it outperforms the traditional statistical models like ARIMA, and machine learning models like XGBoost and SVR methods in both RMSE and the capability of forecasting the sign of price difference. Additionally, to cross-market price difference forecast, the proposed approach has the potential to be applied to solve other forecasting problems such as price spread forecast in DA market for Financial Transmission Right (FTR) trading purpose.

DA/RT price difference↗

Bayesian Structural Time Series for Behind-the-Meter Photovoltaic Disaggregation: Preprint

Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.

Bayesian structural time series↗

Communication Network Layer State Estimation Measurement Model for a Cyber-Secure Smart Grid

Network communication has been proven to be a very important tool and a key factor in the recent development and progress of the power grid operation. It is also considered as the foundation for the smart grid because information and communication are integrated into electricity distribution to achieve reliable and accurate knowledge of the power grid. In previous years, absorbing energy from substations and delivering it to customers was the only type of interaction we knew between utility companies and customers. Presently, the growing connections of small distributed generation units caused by the cost reduction of most of the technologies used in generation and storage of electrical energy, along with the potential benefits of renewable energy have pushed many researchers to look into the improvement of information and communication technologies (ICT) in order to ensure a bidirectional flow of power and data. Moreover, the evolution of information and communication technologies and its applications to smart grid have converted the smart grid into a cyber-physical system where vulnerabilities and additional security challenges such as cyber-threats and cyber-attacks have emerged. Previously, we have demonstrated that using machine learning-based processing on data gathered from communication networks and the power grid was a promising solution for detecting cyber threats by implementing a co-simulation of cyber-security for cross-layer strategy. Since the majority of the challenges observed can only be solved in the network communication layer, we present in this work a physics-based state estimation model of the communication network system towards enhanced cyber-physical security of the smart grid. Information integration with the previously developed machine learning model is developed, providing a enhanced cyber-physical security application for the smart grid. Easy-to-implement model, without hard-to-derive parameters, highlight potential aspects of the model for real-life applications.

Mathieu, Reynold↗