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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region

In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating the Incident Energy of Arcs in Photovoltaic DC Systems: Comparison Between Calculated and Experimental Data

Solar Photovoltaic (PV) systems have permeated the energy generation world at a very high rate, some of the safety codes and standards are still lagging in accurately assessing the hazards and risks associated with PV array arcing energies. Safety professionals and maintenance workers using NFPA 70E have utilized the Doan, Stokes & Oppenlander or Enrique models, meant to determine arc energies in DC power systems using the maximum power method. These methods may lead to an overestimate of energy available in PV systems during a fault. Since PV modules/arrays are non-linear, current limited DC devices, some of these calculation methods may not accurately predict fault energy. This paper will validate current arc energy models for PV systems by comparing experimental and calculated data. Additionally, this data will help modify the current NFPA 70E models related to smaller solar arrays. Understanding where the real safety threshold for DC arc flash in PV systems exists will help maintenance and safety professionals better prepare for a variety of work related activities. This paper will analyze real arc data taken for PV systems <1000VDC and <60amps and compare this to the calculated incident energy models, to include 70E. Thus, using these comparisons, it may be possible to reduce the safety hazard severity and thus relax the PPE requirements for installation and maintenance crews.

14 SOLAR ENERGY↗
DOE OSTI2023

Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications

Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models: Preprint

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to inter-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of said approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating two different clipping correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons will be performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The models can improve accuracy up to 3% in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

clipping↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to subhourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

accuracy↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to sub-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2026

Calculations of enrichment cascade performance using enrichment probabilities – a new method

A new method for calculating the performance of uranium enrichment cascades is presented. The method assigns a unique “enrichment probability” for each isotope to move up or down from the basic enrichment unit, allowing independent material balance calculations for each isotope. The formulation is much simpler than previous methods, which rely on isotopic ratios, and this method can be used when previous methods fail. This method gives the same results as published cases for 235 U enrichment and also gives good agreement with published data on minor isotopes. Some comparisons with measured data and other calculations are given. One case shows that the maximum 235 U enrichment that can be obtained by enrichment of reprocessed uranium (0.02% 234 U initial) is 82%. Another example shows a large difference in the minor isotopic content of material enriched in batches compared to continuous enrichment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Synchronized Waveforms – A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2026

2015 Madison County, Indiana, In the Moment Travel Study

The 2015 In the Moment Travel Study—a pilot study—captured the travel behavior and characteristics of residents in Madison County, Indiana. The Madison County Council of Governments sponsored the study, which was administered by Resource Systems Group and conducted from February to March 2015. It used an activity sampling or "random moments" sampling approach via a smartphone application to capture travel behavior and characteristics from the survey participants. This approach included brief smartphone interactions, e.g., a few minutes per interaction, conducted multiple times a day over multiple days, which was considered less burdensome than traditional household travel diary surveys, which often require 20-30 minutes in one sitting. This proof-of-concept study included households that also participated in the 2014 Heartland in Motion household travel diary survey. Because of this, an assessment of the accuracy and completeness of the collected smartphone application data and comparisons between the "random moments" sample method and traditional household travel surveys can conceivably be drawn.

1Hz data↗
DOE OSTI2024

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

09 BIOMASS FUELS↗
DOE OSTI2020

Density of NaAlSi 2 O 6 Melt at High Pressure and Temperature Measured by In-Situ X-ray Microtomography

In this study, the volumetric compression of jadeite (NaAlSi 2 O 6 ) melt at high pressures was determined by three-dimensional volume imaging using the synchrotron-based X-ray microtomography technique in a rotation-anvil device. Combined with the sample mass, measured using a high-precision analytical balance prior to the high-pressure experiment, the density of jadeite melt was obtained at high pressures and high temperatures up to 4.8 GPa and 1955 K. The density data were fitted to a third-order Birch-Murnaghan equation of state, resulting in a best-fit isothermal bulk modulus K T0 of $10.8$ $^{+1.9}_{–5.3}$ GPa and its pressure derivative K' T0 of $3.4$ $^{+6.6}_{–0.4}$. Comparison with data for silicate melts of various compositions from the literature shows that alkali-rich, polymerized melts are generally more compressible than alkali-poor, depolymerized ones. The high compressibility of jadeite melt at high pressures implies that polymerized sodium aluminosilicate melts, if generated by low-degree partial melting of mantle peridotite at ~250–400 km depth in the deep upper mantle, are likely denser than surrounding mantle materials, and thus gravitationally stable.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Proton removal from Br 73 , 75 to Se 72 , 74 at intermediate energies

Herein we report new experimental data for excited states of 72,74 Se obtained from proton removal from 73,75 Br secondary beams on a proton target. The experiments were performed with the Ursinus-NSCL Liquid Hydrogen Target and the combined GRETINA+S800 setup at the Coupled Cyclotron Facility of the National Superconducting Cyclotron Laboratory at Michigan State University. Within uncertainties, the inclusive cross sections for proton removal from 73,75 Br on a proton target are identical suggesting that the same single-particle orbitals contribute to the proton-removal reaction. In addition, details of the partial cross section fragmentation are discussed. The data might suggest that l = 1, 2, 3, and 4 angular momentum transfers are important to understand the population of excited states of 72,74 Se in proton removal. Available data for excited states of 74 Ge populated through the 75 As(d, 3 He) 74 Ge proton-removal reaction in normal kinematics suggest indeed that the fp and sd shell as well as the 1g 9/2 orbital contribute. A comparison to data available for odd-A nuclei supports that the bulk of the spectroscopic strengths could be found at lower energies in the even-even Se isotopes than in, for instance, the even-even Ge isotopes. In addition, the population of high-J states seems to indicate that multi-step processes contribute to proton-removal reactions at intermediate energies in these collective nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Wave-packet manipulation of He Rydberg states by a seeded free-electron laser

We report a two-dimensional pump-control-probe spectroscopic study of the dynamics of singly excited He Rydberg-state wave packets with a seeded extreme ultraviolet (XUV) free-electron laser (FEL) source. A pair of coherent XUV pulses, defined by their coarse time separation and relative phase, created and manipulated the wave packets. The He atoms were postionized by infrared (IR) pulses, and the ion yield was measured as a function of XUV phase and IR arrival time. We tagged and sorted the relative phase of the XUV pulse pair on a single-shot basis by fitting each FEL spectrum with a suitable function that accounts for nonidealities of the XUV pulse pairs, associated with the seeding process; more generally, the fit returns the time-dependent electric field of the FEL spectra. The experimental two-dimensional maps of ion yields, measured as a function of IR (probe) delay and of XUV (pump-control) phase, were compared with the solution of the first-order time-dependent Schrödinger equation for this field. Despite the fact that the experimental conditions imply strong excitation, beyond the approximations of first-order perturbation theory, the simulated map satisfactorily reproduces the experimental one for temporally well-separated pulses. We show that by selecting data at appropriate values of pump-control phase, we enhance or suppress the amplitude of chosen wave-packet components consisting of two or more Rydberg states. When the temporal overlap of the pulse pair cannot be neglected, the phase reconstruction is underdetermined, and we provide a simplified comparison between data and simulations. Published by the American Physical Society 2024

Dumergue, Mathieu (ORCID:0000000236984406)↗
DOE OSTI2022

Spiner vs EOSPAC6: capabilities, performance, and accuracy considerations

The Cross-Cutting Capabilities Project (XCAP) has significant interest in collecting and implementing a common set of libraries to be used across the various production hydro-codes at LANL. A common set of libraries will help facilitate comparisons between codes. In addition to minimizing variables for comparisons and physics validation, efforts toward optimization and porting of packages for future architectures will be more efficient. As part of this push Singularity is being considered as an inclusive materials interface library. One crucial part of this library is the importing, inverting, and interpolation of equation of state (EOS) data. Currently the Lagrangian Applications Project (LAP) and Safety Applications Project (SAP) are using EOSPAC6 for delivery EOS data. The Eulerian Applications Project (EAP) has historically used TEOS and more recently an implementation of Singularly via a package called Spiner for EOS interpolation. Singularity currently has the option of using several analytical EOS models, directly employing SESAME via Spiner, or directly using SESAME via EOSPAC6 (not optimized yet). Assuming Singularity moves forward as a common platform for the implementation of materials models, a decision will need to be made on which EOS interpolation package XCAP should move forward with, given resources and people are finite. We will attempt to address pros and cons of Spiner and EOSPAC6 and the trade-offs that should be considered during the decision making process. This report is intended as an ASC-PEM-EOS perspective on what is needed in an EOS interpolation package. Performance and Accuracy sections will mostly address data from comparison studies in LA-UR-22-22699.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2023

Northern Hemisphere Snow Drought in Earth System Model Simulations and ERA5‐Land Data in 1980–2014

Abstract Low snow levels over the past few decades and predictions of a low‐to‐no snow future have spurred research into snow droughts, which pose a threat to water security and management. Systematic data‐model comparisons of snow drought have been lacking, hindering our understanding of the drivers of snow drought in the past. To address this gap, we analyzed snow drought events using standardized snow water equivalent index derived from monthly results of four numerical experiments using the E3SM Land Model (ELM) and ERA5‐Land data during the period of 1980–2014. Additionally, we compared snow drought duration calculated from models with those from the ERA5‐Land data during selected El Niño‐Southern Oscillation (ENSO) years. The numerical experiments were conducted with ELM driven by two prescribed atmospheric forcings, and with the coupled land‐atmosphere configuration of E3SM with and without plant hydraulics scheme feedback. Analysis reveals that 20%–30% of snow droughts occur due to factors other than above‐normal temperature and low snowfall, such as low soil moisture, warm soil temperature, and low relative humidity, etc., especially in high latitudes (50° North). Furthermore, our study highlights the exacerbating effect of ENSO events on snow drought conditions in various regions, despite some discrepancies between model and ERA5‐Land results. We also identified limitations of the coupled land‐atmosphere models in our current configuration in capturing the spatial patterns of snow droughts. This study underscores the challenge of predicting and mitigating snow drought and the need for a comprehensive understanding of the factors contributing to snow drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Shock compaction heating and collisional processes in the production of type 3 ordinary chondrites: Lessons from the (nearly) unique L3 chondrite melt breccia Northwest Africa 8709

Abstract Northwest Africa (NWA) 8709 is a rare example of a type 3 ordinary chondrite melt breccia and provides critical information for the shock compaction histories of chondrites. An L3 protolith for NWA 8709 is inferred on the basis of oxygen isotope composition, elemental composition, diverse mineral chemistry, and overall texture. NWA 8709 is among the most strongly shocked type 3 chondrites known, and experienced complete melting of the matrix and partial melting of chondrules. Unmelted phases underwent FeO reduction and partial homogenization, with reduction possibly occurring by reaction of olivine and low‐Ca pyroxene with an S‐bearing gas that was produced by vaporization. Chondrules and metal grains became foliated by uniaxial compaction, and during compression, chondrules and fragments became attached to form larger clumps. This process, and possibly also melt incorporation into chondrules to cause “inflation,” may have contributed to anomalously large chondrule sizes in NWA 8709. The melt breccia character is attributed to strong shock affecting a porous precursor. Data‐model comparisons suggest that a precursor with 23% porosity that was impacted by a 3 km/s projectile could have produced the meteorite. The rarity of other type 3 ordinary chondrite melt breccias implies that the immediate precursors to such chondrites were lower in porosity than the NWA 8709 precursor, or experienced weaker shocks. Altogether, the data imply a predominantly “quiet” dynamical environment to form most type 3 ordinary chondrites, with compaction occurring in a series of relatively weak shock events.

Ruzicka, Alex M.↗
DOE OSTI2020

CsI(Tl) pulse shape discrimination with the Belle II electromagnetic calorimeter as a novel method to improve particle identification at electron–positron colliders

Here we describe the implementation and performance of CsI(Tl) pulse shape discrimination for the Belle II electromagnetic calorimeter, representing the first application of CsI(Tl) pulse shape discrimination for particle identification at an electron–positron collider. The pulse shape characterization algorithms applied by the Belle II calorimeter are described. Control samples of $γ, μ^+, π^±, K^±$ and $ρ/ \bar{ρ}$ are used to demonstrate the significant insight into the secondary particle composition of calorimeter clusters that is provided by CsI(Tl) pulse shape discrimination. Comparisons with simulation are presented and provide further validation for newly developed CsI(Tl) scintillation response simulation techniques, which when incorporated with GEANT4 simulations allow the particle dependent scintillation response of CsI(Tl) to be modelled. Comparisons between data and simulation also demonstrate that pulse shape discrimination can be a new tool to identify sources of improvement in the simulation of hadronic interactions in materials. The $K^0_L$ efficiency and photon-as-hadron fake-rate of a multivariate classifier that is trained to use pulse shape discrimination is presented and comparisons are made to a shower-shape based approach. CsI(Tl) pulse shape discrimination is shown to reduce the photon-as-hadron fake-rate by over a factor of 3 at photon energies of 0.2 GeV and over a factor 10 at photon energies of 1 GeV.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗