Search NASA⌕ Search

SEARCH · Search NASA

Results for “differentiable programming”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9

Translational Systems Pharmacology‐Based Predictive Assessment of Drug‐Induced Cardiomyopathy

Drug‐induced cardiomyopathy contributes to drug attrition. We compared two pipelines of predictive modeling: (1) applying elastic net (EN) to differentially expressed genes (DEGs) of drugs; (2) applying integer linear programming (ILP) to construct each drug's signaling pathway starting from its targets to downstream proteins, to transcription factors, and to its DEGs in human cardiomyocytes, and then subjecting the genes/proteins in the drugs' signaling networks to EN regression. We classified 31 drugs with availability of DEGs into 13 toxic and 18 nontoxic drugs based on a clinical cardiomyopathy incidence cutoff of 0.1%. The ILP‐augmented modeling increased prediction accuracy from 79% to 88% (sensitivity: 88%; specificity: 89%) under leave‐one‐out cross validation. The ILP‐constructed signaling networks of drugs were better predictors than DEGs. Per literature, the microRNAs that reportedly regulate expression of our six top predictors are of diagnostic value for natural heart failure or doxorubicin‐induced cardiomyopathy. This translational predictive modeling might uncover potential biomarkers.

59 BASIC BIOLOGICAL SCIENCES↗

Unique phase identification of trimetallic copper iron manganese oxygen carrier using simultaneous differential scanning calorimetry/thermogravimetric analysis during chemical looping combustion reactions with methane

Chemical looping combustion (CLC) is a promising combustion technology that generates heat and sequestration-ready carbon dioxide that is undiluted by nitrogen from the combustion of carbonaceous fuels with an oxygen carrier, or metal oxide. This process is highly dependent on the reactivity and stability of the oxygen carrier. The development of oxygen carriers remains one of the major barriers for commercialization of CLC. Synthetic oxygen carriers, consisting of multiple metal components, have demonstrated enhanced performance and improved CLC operation compared to single metal oxides. However, identification of the complex mixed metal oxide phases that form after calcination or during CLC reactions has been challenging. Without an understanding of the dominant metal oxide phase, it is difficult to determine reaction parameters and the oxygen carrier reduction pathway, which are necessary for CLC reactor design. This is particularly challenging for complex multi-component oxygen carriers such as copper iron manganese oxide (CuFeMnO 4 ). This study aims to differentiate the unique phase formation of a highly reactive, complex trimetallic oxygen carrier, CuFeMnO 4 , from its single and bimetallic counterparts using thermochemical and reaction data obtained from simultaneous differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) during temperature programmed reductions (TPR) with methane. DSC/TGA experiments during TPR with methane provides heat flow data and corresponding reaction rate data that can be used to determine reaction routes and mechanisms during methane reduction. Furthermore, non-isothermal TPR data provides the advantage of distinguishing reactions that may not be observable in isothermal analysis. The detailed thermochemical and reaction data, obtained during TPR with methane, distinguished a unique reduction pathway for CuFeMnO 4 that differed from its single and bimetallic counterparts. This is remarkable since X-ray diffraction (XRD) data alone could not be used to distinguish the reactive trimetallic oxide phase due to overlapping peaks from various single and mixed metal oxides. The unique reduction pathway of CuFeMnO 4 was further characterized in this study using in-situ XRD TPR with methane to determine changes in the dominant trimetallic phase that influenced the thermochemical and reaction rate data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Poisson_CCD: A dedicated simulator for modeling CCDs

A dedicated simulator, Poisson_CCD, has been constructed which models astronomical CCDs by solving Poisson’s equation numerically and simulating charge transport within the CCD. The potentials and free carrier densities within the CCD are self-consistently solved for, giving realistic results for the charge distribution within the CCD storage wells. The simulator has been used to model the CCDs that are being used to construct the LSST digital camera. The simulator output has been validated by comparing its predictions with several different types of CCD measurements, including astrometric shifts, brighter-fatter induced pixel–pixel covariances, saturation effects, and diffusion spreading. The code is open source and freely available.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations and splice variants identified in cancer cells are translated. Herein we therefore describe a proteogenomic data integration tool (QUILTS) and illustrate its application to whole genome, transcriptome and global MS peptide sequence datasets generated from a pair of luminal and basal-like breast cancer patient derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS process replicates. Despite over thirty sample replicates, only about 10% of all SNV (somatic and germline) were detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNV without a detectable mRNA transcript were also observed demonstrating the transcriptome coverage was also incomplete (~80%). In contrast to germ-line variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than the luminal tumor raising the possibility of differential translation or protein degradation effects. In conclusion, the QUILTS program integrates DNA, RNA and peptide sequencing to assess the degree to which somatic mutations are translated and therefore biologically active. By identifying gaps in sequence coverage QUILTS benchmarks current technology and assesses progress towards whole cancer proteome and transcriptome analysis.

Environmental Molecular Sciences Laboratory↗

FSEN Reaction Rate Calculations in MCNP [Slides]

FSEN provides an opportunity to quickly calculate sensitivities to orthogonal measurements during integral experiments. Toy problem has been deployed to expand on verification of FSEN for reaction rate ratios. While there is decent agreement, further investigation must be done on multiplication’s impact on sensitivity vector.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

CRISPR/Cas9 Directed Reprogramming of iPSC for Accelerated Motor Neuron Differentiation Leads to Dysregulation of Neuronal Fate Patterning and Function

Neurodegeneration causes a significant disease burden and there are few therapeutic interventions available for reversing or slowing the disease progression. Induced pluripotent stem cells (iPSCs) hold significant potential since they are sourced from adult tissue and have the capacity to be differentiated into numerous cell lineages, including motor neurons. This differentiation process traditionally relies on cell lineage patterning factors to be supplied in the differentiation media. Genetic engineering of iPSC with the introduction of recombinant master regulators of motor neuron (MN) differentiation has the potential to shorten and streamline cell developmental programs. We have established stable iPSC cell lines with transient induction of exogenous LHX3 and ISL1 from the Tet-activator regulatory region and have demonstrated that induction of the transgenes is not sufficient for the development of mature MNs in the absence of neuron patterning factors. Comparative global transcriptome analysis of MN development from native and Lhx-ISL1 modified iPSC cultures demonstrated that the genetic manipulation helped to streamline the neuronal patterning process. However, leaky gene expression of the exogenous MN master regulators in iPSC resulted in the premature activation of genetic pathways characteristic of the mature MN function. Dysregulation of metabolic and regulatory pathways within the developmental process affected the MN electrophysiological responses.

59 BASIC BIOLOGICAL SCIENCES↗

Solving differential‐algebraic equations in power system dynamic analysis with quantum computing

Abstract Power system dynamics are generally modeled by high dimensional non‐linear differential‐algebraic equations (DAEs) given a large number of components forming the network. These DAEs' complexity can grow exponentially due to the increasing penetration of distributed energy resources, whereas their computation time becomes sensitive due to the increasing interconnection of the power grid with other energy systems. This paper demonstrates the use of quantum computing algorithms to solve DAEs for power system dynamic analysis. We leverage a symbolic programming framework to equivalently convert the power system's DAEs into ordinary differential equations (ODEs) using index reduction methods and then encode their data into qubits using amplitude encoding. The system non‐linearity is captured by Hamiltonian simulation with truncated Taylor expansion so that state variables can be updated by a quantum linear equation solver. Our results show that quantum computing can solve the power system's DAEs accurately with a computational complexity polynomial in the logarithm of the system dimension. We also illustrate the use of recent advanced tools in scientific machine learning for implementing complex computing concepts, that is, Taylor expansion, DAEs/ODEs transformation, and quantum computing solver with abstract representation for power engineering applications.

computational complexity↗

Comparative evaluation of analogue front-end designs for the CMS Inner Tracker at the High Luminosity LHC

The CMS Inner Tracker, made of silicon pixel modules, will be entirely replaced prior to the start of the High Luminosity LHC period. One of the crucial components of the new Inner Tracker system is the readout chip, being developed by the RD53 Collaboration, and in particular its analogue front-end, which receives the signal from the sensor and digitizes it. Three different analogue front-ends (Synchronous, Linear, and Differential) were designed and implemented in the RD53A demonstrator chip. A dedicated evaluation program was carried out to select the most suitable design to build a radiation tolerant pixel detector able to sustain high particle rates with high efficiency and a small fraction of spurious pixel hits. The test results showed that all three analogue front-ends presented strong points, but also limitations. The Differential front-end demonstrated very low noise, but the threshold tuning became problematic after irradiation. Moreover, a saturation in the preamplifier feedback loop affected the return of the signal to baseline and thus increased the dead time. The Synchronous front-end showed very good timing performance, but also higher noise. For the Linear front-end all of the parameters were within specification, although this design had the largest time walk. This limitation was addressed and mitigated in an improved design. The analysis of the advantages and disadvantages of the three front-ends in the context of the CMS Inner Tracker operation requirements led to the selection of the improved design Linear front-end for integration in the final CMS readout chip.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

NCSP Nuclear Data Program [Slides]

This talk covers wide-ranging information on the Nuclear Criticality Safety Program (NCSP) Data Program. The Lecture includes slides on the history and mission of the NCSP along with charts and graphs on budgets from 2005 onwards. This lecture additionally mentions NCSP critical assemblies, differential experiments, and data measurements and evaluation work. The talk concludes with a gaze into NCSP benefits and successes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Distributed Solar Incentive Programs: Recent Experience and Best Practices for Design and Implementation

Based on lessons from recent program experience, this report explores best practices for designing and implementing incentives for small and mid-sized residential and commercial distributed solar energy projects. The findings of this paper are relevant to both new incentive programs as well as those undergoing modifications. The report covers factors to consider in setting and modifying incentive levels over time, differentiating incentives to encourage various market segments, administrative issues such as providing equitable access to incentives and customer protection. It also explores how incentive programs can be designed to respond to changing market conditions while attempting to provide a longer-term and stable environment for the solar industry. The findings are based on interviews with program administrators, regulators, and industry representatives as well as data from numerous incentive programs nationally, particularly the largest and longest-running programs. These best practices consider the perspectives of various stakeholders and the broad objectives of reducing solar costs, encouraging long-term market viability, minimizing ratepayer costs, and protecting consumers.

14 SOLAR ENERGY↗

CCAR1 is required for Ngn3-mediated endocrine differentiation

Highlights: Black-Right-Pointing-Pointer We identify CCAR1 to directly interact with Ngn3. Black-Right-Pointing-Pointer CCAR1 is co-localized with Ngn3 in the nucleus. Black-Right-Pointing-Pointer CCAR1 cooperates with Ngn3 in activating NeuroD expression. Black-Right-Pointing-Pointer CCAR1 is required for Ngn3-mediated PANC-1 transdifferentiation. -- Abstract: Neurogenin3 (Ngn3) is a basic helix-loop-helix transcription factor that specifies pancreatic endocrine cell fates during pancreas development. It can also initiate a transdifferentiation program when expressed in pancreatic exocrine and ductal cells. However, how Ngn3 initiates a transcriptional cascade to achieve endocrine differentiation is still poorly understood. Here, we show that cell cycle and apoptosis regulator 1 (CCAR1), which is a transcriptional coactivator for nuclear receptors, also interacts with Ngn3. The association between Ngn3 and CCAR1 was verified by pull-down assays and co-immunoprecipitation analyses. Using gene reporter assays, we found that CCAR1 is essential for Ngn3 to activate the expression of the reporter genes containing the NeuroD promoter. Moreover, down-regulation of endogenous CCAR1 in the PANC-1 pancreatic ductal cell line inhibits the transdifferentiation program initiated by Ngn3. CCAR1 is, therefore, a novel partner of Ngn3 in mediating endocrine differentiation.

60 APPLIED LIFE SCIENCES↗

Disruption of canonical TGFβ-signaling in murine coronary progenitor cells by low level arsenic

Exposure to arsenic results in several types of cancers as well as heart disease. A major contributor to ischemic heart pathologies is coronary artery disease, however the influences by environmental arsenic in this disease process are not known. Similarly, the impact of toxicants on blood vessel formation and function during development has not been studied. During embryogenesis, the epicardium undergoes proliferation, migration, and differentiation into several cardiac cell types including smooth muscle cells which contribute to the coronary vessels. The TGFβ family of ligands and receptors is essential for developmental cardiac epithelial to mesenchymal transition (EMT) and differentiation into coronary smooth muscle cells. In this in vitro study, 18 hour exposure to 1.34 μM arsenite disrupted developmental EMT programming in murine epicardial cells causing a deficit in cardiac mesenchyme. The expression of EMT genes including TGFβ2, TGFβ receptor-3, Snail, and Has-2 are decreased in a dose-dependent manner following exposure to arsenite. TGFβ2 cell signaling is abrogated as detected by decreases in phosphorylated Smad2/3 when cells are exposed to 1.34 μM arsenite. There is also loss of nuclear accumulation pSmad due to arsenite exposure. These observations coincide with a decrease in vimentin positive mesenchymal cells invading three-dimensional collagen gels. However, arsenite does not block TGFβ2 mediated smooth muscle cell differentiation by epicardial cells. Overall these results show that arsenic exposure blocks developmental EMT gene programming in murine coronary progenitor cells by disrupting TGFβ2 signals and Smad activation, and that smooth muscle cell differentiation is refractory to this arsenic toxicity. - Highlights: • Arsenic blocks TGFβ2 induced expression of EMT genes. • Arsenic blocks TGFβ2 triggered Smad2/3 phosphorylation and nuclear translocation. • Arsenic blocks epicardial cell differentiation into cardiac mesenchyme. • Arsenic does not block TGFβ2 induced smooth muscle cell differentiation.

60 APPLIED LIFE SCIENCES↗

Glass structure and crystallization in boro-alumino-silicate glasses containing rare earth and transition metal cations: a US-UK collaborative program

Nuclear wastes generated from reprocessing of used nuclear fuel tend to contain a large fraction of rare earth (RE, e.g., Nd3+), transition (TM, e.g., Mo6+, Zr4+), alkali (A, e.g., Cs+), and alkaline earth cations (AE, e.g., Ba2+, Sr2+). Various strategies have been considered for immobilizing such waste streams, varying from nominally crystal-free glass to glass-ceramic to multi-phase ceramic waste forms. For glass and glass-ceramic waste forms, the added glass-forming system is generally alkali-alkaline earth-aluminoborosilicate (i.e., Na-Ca-Al-B-Si oxide). In a US-UK collaborative project, summarized here, we investigated the glass structure and crystallization dependence on compositional changes in simulated nuclear waste glasses and glass-ceramics. Compositions ranged in complexity from five – to – eight oxides. Specifically, the roles of Mo and rare earths are investigated, since a proposed glass-ceramic waste form contains crystalline phases such as powellite [(AE,A,RE)MoO4] and oxyapatite [(RE,AE,A)10Si6O26], and the precipitation of molybdenum phases is known to be affected by the rare earth concentration in the glass. Additionally, the effects of other chemical additions have been systematically investigated, including Zr, Ru, P, and Ti. A series of studies were also undertaken to ascertain the effect of the RE size on glass structure and on partitioning to crystal phases, investigating similarities and differences in glasses containing single RE oxides of Sc, Y, La, Ce, Nd, Sm, Er, Yb, or Lu. Finally, the effect of charge compensation was investigated by considering not only the commonly assessed peralkaline glass but also metaluminous and peraluminous compositions. Glass structure and crystallization studies were conducted by spectroscopic methods (i.e., Raman, X-ray absorption, nuclear magnetic resonance (NMR), optical absorption, photoluminescence, photoluminescence excitation, X-ray photoelectron spectroscopy), microscopy (i.e., scanning electron microscopy, transmission electron microscopy, electron probe microanalysis), scattering (i.e., X-ray and neutron diffraction, small angle measurements), and physical characterization (i.e., differential thermal analysis, liquidus, viscosity, density). This paper will give an overview of the research program and some example unpublished results on glass-ceramic crystallization kinetics, microstructure, and Raman spectra, as well as some examples of the effects of rare earths on the absorption, luminescence, and NMR spectra of starting glasses. The formal collaboration described here has resulted in the generation of a large number of results, some of which are still in the process of being published as separate studies.

McCloy, John S.↗

The PARADIGM Project: Case Study in Balancing Experiment Uncertainty with Design simplicity

Accurate nuclear data are required for simulations of many applications including nuclear criticality safety. Actinide nuclear data at intermediate energies (from 1 to 100s of keV) are imprecise and inaccurate, because of scarce differential data, and an insufficient theory approach to capture the structures expected in the data to yield evaluated nuclear data, and lack of integral data for proper validation. This is a known deficiency but has proved challenging to address. More specifically, only 5% of integral experiments in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) benchmark suite address intermediate energies (Fig. 1). Associated calculated effective multiplication factor, k eff , values for these experiments are far outside the experimental uncertainties and are 25× further from experiment than for fast energies. These differences could either stem from systematic biases in nuclear data, experiments or both. The goal of the PARADIGM (PARallel Approach of Differential and InteGral Measurements) project is to significantly reduce (by more than tens of percent) the uncertainties of intermediate energy actinide nuclear data. The PARADIGM project designed and intends to execute LANSCE (Los Alamos Neutron Science CEnter) and NCERC (National Criticality Experiments Research Center) intermediate experiments in parallel. They will specifically address a high priority nuclear data need—reducing bias and uncertainty in intermediate plutonium nuclear data. The two experiment will achieve that by informing each other and nuclear theory. By doing all these steps in parallel, the timeline to deliver improved nuclear data to users will significantly be reduced. This work will focus on the integral experiment final design and the balance of design and modeling simplicity while minimizing experiment uncertainty.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Winners are not keepers: Characterizing household engagement, gains, and energy patterns in demand response using machine learning in the United States

Demand-response programs can help utilities manage rapidly evolving electric grids, but these programs are subject to the complexities of human behavior. This paper explores a novel method for uncovering heterogeneity in households. In this work, we use a machine-learning method known as a Conditional Inference Tree (c-tree) algorithm to categorize households based on their energy behavior characteristics collected via smart meters, and explore how this translates through into heterogeneity in their real-world response to a DR program. Using data from randomized controlled trial, we generate estimates of the changes in energy use caused by the program within each household group. Our results show that the c-tree approach differentiates households by their energy-use characteristics in a way that increases the spread in enrollment rates and critical peak reduction among household groups, compared with the spreads achieved via several conventional segmentation methods. Thus, the c-tree analysis enables the most tailored targeting of major potential energy savers and could provide the greatest increase in cost-effectiveness of household recruitment into DR programs. Our results also offer fresh insights into the relationships between household energy behavior characteristics – such as peak energy use and “structural winningness” (the ability to save money under a DR program without changing energy-use behaviors) – and household decisions about enrolling in DR programs and reducing energy use. Our research also demonstrates the potential of smart meter data, combined with machine learning and econometric methods, to provide significant value to utilities, program implementers, researchers, and other stakeholders.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Process Anomaly Detection for Sparsely Labeled Events in Nuclear Power Plants

An essential aspect of online monitoring, subtle anomaly detection increases the detection lead time for equipment failure and enables a nuclear power plant (NPP) to mitigate unexpected partial or full outages, resulting in significant cost saving to the plant. Once an anomaly is detected by plant staff, its cause and severity are investigated. Because the vast majority of anomalies require some level of investigation, including some that require time-consuming examination, before they are passed over to the engineering organization for further analysis, plants are often equipped with tools to assist the staff in performing anomaly detection. Those tools operate as a black box and are often based on statistical methods that establish sensor correlations using preconfigured mathematical models and flag correlation deviations as anomalies. Due to the number of anomalies detected at a given NPP on a daily basis, a significant number of flagged anomalies usually await examination for days or weeks. A primary cause of this backlog is that the methods used by the tools generate many false positives. Though this is usually attributed to oversensitive model settings due to very narrow normal operation bands, it can also be associated with the model development being inadequate for the process being monitored, or with missing model inputs that could have explained misclassified positives. The performance of anomaly detection tools impacts their plant acceptance and utilization, especially when the effort to address false positives generated by the tool depletes the value or cost saved by using that tool. Thus, means to advance anomaly detection performance have been investigated by the Department of Energy’s Light Water Reactor Sustainability program. Previous and ongoing efforts have targeted unsupervised machine-learning (ML) methods, which do not require the labeling of any data fed into the ML model. By contrast, in supervised anomaly detection methods, every data point is labeled as either a normal or abnormal process condition, and the model is trained to replicate the classification process. Supervised methods usually outperform unsupervised methods, due to the added value in differentiating normal from anomalous states of the monitored process. An NPP’s corrective action program requires it to track and document, via a dedicated report, the resolution of any issues that occur within the plant. Once created, each report is reviewed by a plant screening committee, and several classifications and decisions are made. Recently, a collaborating NPP developed an artificial intelligence and ML-based classifier to categorize a condition report (CR) into classes that can serve to label the data as normal or anomalous. Applying CRs as labels represents a semi-supervised use case. Semi-supervised ML assumes that labels exist for some data points (i.e., labeled anomalies, in this case) but not for the rest. In this effort, semi-supervised ML methods were used to fuse data from CRs with anomaly detection methods in order to test the hypothesis that partially labeled anomalies would improve the accuracy of the anomaly detection methods. Specifically, two methods were used. The first is the deep Semi-supervised Anomaly Detection (deep SAD) method, which can handle labels ranging from fully unsupervised to fully supervised cases. The second is a newly designed ML method developed specifically for this effort and referred to as the high-order feature (HOF)-based method. To evaluate these two methods in controlled environments, synthetic data generators were developed and used. The first datasets used a spring-mass-damper (SMD) system simulator commonly found in mechanical engineering references. This was used to create two use cases: a one- and a three-mass system. Anomalies were introduced by changing the spring and damper coefficients while the system was actuated by random forces. The second datasets used the commercial Dymola-Modelica software to build a simplified nuclear reactor model. Anomalies were added in the form of corrupted sensor readings and/or control commands. The deep SAD method was tested using the SMD system, while the HOF method was tested using both datasets. Application of the deep SAD semi-supervised ML method demonstrated that labels can generate increased confidence in detecting true anomalies. This helped increase the number of true positives and decrease the number of false negatives—something that would aid in addressing the backlog of possible anomalies. Application of the HOF method demonstrated that labels can aid in down selecting from a candidate set of features to a more optimal subset in order to better differentiate between normal and anomalous conditions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Exclusive dielectron production in ultraperipheral Pb+Pb collisions at $\sqrt{s_{\textrm{NN}}}$ = 5.02 TeV with ATLAS

Exclusive production of dielectron pairs, γγ → e + e – , is studied using $\mathcal{L}_{int}$ = 1.72 nb –1 of data from ultraperipheral collisions of lead nuclei at $\sqrt{s_{NN}}$ = 5.02 TeV recorded by the ATLAS detector at the LHC. The process of interest proceeds via photon–photon interactions in the strong electromagnetic fields of relativistic lead nuclei. Dielectron production is measured in the fiducial region defined by following requirements: electron transverse momentum $p^{e}_{T}$ > 2.5 GeV, absolute electron pseudorapidity |η e | < 2.5, dielectron invariant mass m ee > 5 GeV, and dielectron transverse momentum $p^{ee}_{T}$ < 2 GeV. Differential cross-sections are measured as a function of m ee , average $p^{e}_{T}$, absolute dielectron rapidity |y ee |, and scattering angle in the dielectron rest frame, |cos θ*|, in the inclusive sample, and also with a requirement of no activity in the forward direction. The total integrated fiducial cross-section is measured to be 215 ± 1 (stat) $^{+23}_{-20}$ (syst.) ± 4 (lumi.) μ b. Within experimental uncertainties the measured integrated cross-section is in good agreement with the QED predictions from the Monte Carlo programs STARLIGHT and SUPERCHIC, confirming the broad features of the initial photon fluxes. The differential cross-sections show systematic differences from these predictions which are more pronounced at high |y ee | and |cos θ*| values.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗