The universe at faint magnitudes. I - Models for the galaxy and the predicted star counts
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Bahcall, J. N.↗
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XTE Observations of PSR 1259-63 and a Test of Spin Orbit Coupling in the 4U0115+63 System
During this report period, Mallory Roberts went to GSFC to analyze the data from two minor outbursts, which occurred from 4UO115+63. Unfortunately, the outbursts were not of sufficient duration to do a unique orbital determination (which was the scientific goal of the experiment). As this report is being written, 4UO115+63 is undergoing its first major outburst in four years. We are planning on adding our RXTE PCA data to any public ASM or PCA data that is obtained through the duration of this outburst, and combining it with our BATSE data from 1994 and 1995 outbursts in order to learn something about the orbital evolution in this system. We have formed a collaboration with colleagues at MIT who are working on the ASM data for this outburst. Thus, work on the original data will continue, with no further funding, and we are hopeful that some important questions with regard to the orbital timing will finally be resolved. The PSR 1259-63 data were originally analyzed by Barry Giles, who reported that no pulsations or flux were seen from this source near apastron. Recently, a new background model for low-count rate sources has been developed for the PCA. We intend to use this new background model to reanalyze these data to see if we can improve the upper limit to the flux. This work will also continue with no further funding.
Cominsky, Lynn R.↗
On the analysis of signal peaks in pulse-height spectra
The estimation of the signal location and intensity of a peak in a pulse height spectrum is important for X- ray and γ-ray spectroscopy, charged-particle spectrometry, liquid chromatography, and many other subfields. However, both the ‘‘centroid’’ and ‘‘signal intensity’’ of a peak in a pulse-height spectrum are ill-defined quantities and different methods of analysis will yield different numerical results. Here, we apply three methods of analysis. Method A is based on simple count summation and is likely the technique most frequently applied in practice. The analysis is straightforward and fast, and does not involve any statistical modeling. We find that it provides reliable results only for high signal-to-noise data, but has severe limitations in all other cases. Method B employs a Bayesian model to extract signal counts and centroid from the measured total and background counts. The resulting values are derived from the respective posteriors and, therefore, have a rigorous statistical meaning. The method makes no assumptions about the peak shape. It yields reliable and relatively small centroid uncertainties. However, it provides relatively large signal count uncertainties. Method C makes a strong assumption regarding the peak shape by fitting a Gaussian function to the data. Furthermore, the fit is based again on a Bayesian model. Although Method C requires careful consideration of the Gaussian width (usually given by the detector resolution) used in the fitting, it provides reliable values and relatively small uncertainties both for the signal counts and the centroid.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment
Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
machine learning↗
Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment
Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.
machine learning↗
A new G–M counter hybrid dead-time correction model
In this paper, a new G–M counter dead-time model based on the hybrid model is proposed and studied. The new model contains the idealized paralyzable and non-paralyzable dead-time models, but also allows the non-paralyzable dead-time to vary with the true counting rate. To validate this model, the decaying source experiment with a very pure 56 Mn source was used. The calculated true counting rates obtained by fitting the experimental data to the model were compared to the data obtained by table lookup. Finally, in general, the fitting was in good agreement (within 4.8% up to 5.35 × 10 6 counts / minute ) for a G–M counter with a paralyzable dead-time component of 85.9 μs and a non-paralyzable dead-time component ranging from 438 μs to 24.3 ms.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Expected individual benefit of prophylactic platelet transfusions in hemato‐oncology patients based on bleeding risks
Abstract Background Prophylactic platelet transfusions prevent bleeding in hemato‐oncology patients, but it is unclear how any benefit varies between patients. Our aim was to assess if patients with different baseline risks for bleeding benefit differently from a prophylactic platelet transfusion strategy. Study design and methods Using the data from the randomized controlled TOPPS trial (Trial of Platelet Prophylaxis), we developed a prediction model for World Health Organization grades 2, 3, and 4 bleeding risk (defined as at least one bleeding episode in a 30 days period) and grouped patients in four risk‐quartiles based on this predicted baseline risk. Predictors in the model were baseline platelet count, age, diagnosis, disease modifying treatment, disease status, previous stem cell transplantation, and the randomization arm. Results The model had a c‐statistic of 0.58 (95% confidence interval [CI] 0.54–0.64). There was little variation in predicted risks (quartiles 46%, 47%, and 51%), but prophylactic platelet transfusions gave a risk reduction in all risk quartiles. The absolute risk difference (ARD) was 3.4% (CI −12.2 to 18.9) in the lowest risk quartile (quartile 1), 7.4% (95% CI −8.4 to 23.3) in quartile 2, 6.8% (95% CI −9.1 to 22.9) in quartile 3, and 12.8% (CI −3.1 to 28.7) in the highest risk quartile (quartile 4). Conclusion In our study, generally accepted bleeding risk predictors had limited predictive power (expressed by the low c‐statistic), and, given the wide confidence intervals of predicted ARD, could not aid in identifying subgroups of patients who might benefit more (or less) from prophylactic platelet transfusion.
Cornelissen, Loes L.↗
Impact Real World System Validation
Introduction NASA has developed a new evidence-based data-driven probabilistic risk assessment and tradespace analysis tool as a successor to the Integrated Medical Model. This updated decision support tool is known as IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces). IMPACT estimates the frequency and consequences of medical conditions that might arise during exploration missions. A validation analysis of IMPACT was performed with respect to a set of International Space Station (ISS) and Shuttle Transportation System (STS) real world system (RWS) referent data due to the limited referent data available from exploration missions. Methods Observed mission and crew characteristics from STS and ISS missions were used as model inputs within MEDPRAT (Medical Extensible Dynamic Probabilistic Risk Assessment Tool). For each mission, two hundred thousand simulations were generated. For each mission, model outputs included occurrence counts for each condition, total medical events (TME), and the probability of loss of crew life (LOCL). These simulated model outputs were compared to the RWS referent data. Results The predicted number of total medical events exceeded the total RWS medical events for ISS missions and combined ISS and STS missions and fell within the 90% confidence interval for STS missions. For the 32 ISS missions simulated by IMPACT, the number of total medical events was overpredicted for 19 missions and fell within the 90% confidence interval for 13 missions. For the 21 STS missions, the total number of medical events was overpredicted for 3 missions, fell within the 90% confidence interval for 16 missions, and was underpredicted for 2 missions. Combined, 29 missions were in range, 22 were overpredicted, and 2 were underpredicted. The predicted LOCL probability for the 32 ISS missions, the 21 STS missions, and the combined ISS and STS missions was consistent with the zero LOCL events observed in the RWS referent data. The validation analysis included a comparison of the number of medical events predicted by IMPACT and the number of medical events observed in the RWS data on a condition-by-condition basis. For ISS missions, 50 conditions were in range, 52 conditions were statistically underpowered (not enough observed sample to draw any conclusions on precision), 8 conditions were overpredicted, and 9 conditions were underpredicted. Overall, only 14% (17/119) of conditions were out of range for STS missions, 40 conditions were in range, 59 conditions were statistically underpowered, 10 conditions were overpredicted, and 10 conditions were underpredicted. Overall, only 17% (20/119) of conditions were out of range. For combined ISS and STS missions, 11 conditions were overpredicted, and 11 conditions were underpredicted. Overall, only 18% (22/119) of conditions were out of range. For combined ISS and STS missions, 49 conditions were in range, 46 conditions were statistically underpowered, 18 conditions were overpredicted, and 8 conditions were underpredicted. Overall, 21% (26/121) of conditions were out of range. Conclusion The results of this validation analysis should not be interpreted as a pass/fail test of the validity of IMPACT. Instead, this validation analysis should be used to assess some of the IMPACT outcomes in terms of consistencies and inconsistencies with the ISS and STS RWS referent data.
L. Boley↗
Application of Neutron Multiplicity Counting Experiments to Optimal Cross-Section Adjustments
This paper presents the first application of model calibration to neutron multiplicity counting (NMC) experiments for cross-section optimization that is informed by adjoint-based sensitivity analysis (SA) and first-order uncertainty quantification (UQ). We summarize previous work on SA applied to NMC and describe notable modifications and additions. We give the procedure for first-order UQ and Bayesian-inference-based parameter estimation (PE). We then discuss model calibration applied to NMC of a 4.5-kg sphere of weapons-grade, alpha-phase plutonium metal (the BeRP ball) with the nPod neutron multiplicity counter. For the BeRP ball in bare and polyethylene-reflected configurations, we discuss the sensitivity of the first- and second-moment detector responses (i.e., first and second moments of the NMC distribution, respectively) to the cross sections. We describe the sources of uncertainty in the measured and simulated responses. Specifically, uncertainty in the measured responses is due to both random and systematic sources. Uncertainty in the simulated responses is due to the cross-section covariances. We describe in detail the adjustment to the cross sections and cross-section covariances due to the optimization. Due to the contribution of systematic uncertainties to the measured response uncertainties, the adjustment to the cross sections is similar in trend but larger in magnitude compared to that recommended by previous work. We compare the measured responses to responses simulated with nominal and optimized cross sections, demonstrating that the best-estimate cross sections produce simulations of NMC experiments that are more accurate with reduced uncertainty.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Contemporaneous IUE, EUVE, and High-Energy Observations of 3C 273
We present the results of our 1994 January and 1995 January observations of the quasar 3C 273 obtained with the International Ultraviolet Explorer (IUE) and the Extreme-Ultraviolet Explorer (EUVE). These observations were part of a large multiwavelength campaign to observe 3C 273 from radio through gamma-rays. Our 1995 January photometric observations with the EUVE Lexan/B Deep Survey (DS) instrument indicate strong evidence for variability, at a 99% confidence level, during the 12 day observing period. We have utilized ROSAT PSPC soft X-ray power-law models to correlate with EUVE count rates. Besides variations in the normalization level between both observations, our EUV count rates are consistent with a simple power-law model with spectral index alpha approx. 1.77 (F(sub upsilon) proportional to upsilon(sup -alpha) that can be extrapolated from the soft X-rays to the EUV range. The active galactic nucleus 3C 273 is an important blazar to study because in our picture it reveals the presence of both disk and relativistic beam spectral contributions.
Ramos, E.↗
Efficient IMEX Runge-Kutta Methods for Nonhydrostatic Dynamics
We develop new implicit-explicit Runge-Kutta (IMEX RK) methods for integrating horizontally explicit, vertically implicit (HEVI) partitionings of nonhydrostatic atmosphere models (HEVI models). These new methods, termed IMKG methods, are IMEX RK methods whose explicit part has optimal or near-optimal stability on the imaginary axis and whose implicit part is I-stable. A specialized stability region is presented for characterizing the stability of IMKG and other IMEX methods integrating HEVI models. Subsequently, we formulate two families of IMEX RK methods to enable deriving IMKG methods with a high explicit stage count for integrating HEVI models with large, stable time-steps. Here, we then derive a HEVI partitioning of the HOMME-NH nonhydrostatic model and use this model to compare the accuracy and efficiency of several IMKG methods with other IMEX RK methods from the literature.
58 GEOSCIENCES↗
Emission Features and Source Counts of Galaxies in Mid-Infrared
In this work we incorporate the newest ISO results on the mid-infrared spectral-energy-distributions (MIR SEDs) of galaxies into models for the number counts and redshift distributions of MIR surveys.
mid-infrared spectral-energy-distributions number ↗
A FIRST ESTIMATE OF RADIO HALO STATISTICS FROM LARGE-SCALE COSMOLOGICAL SIMULATION
We present a first estimate based on a cosmological gas dynamics simulation of galaxy cluster radio halo counts to be expected in forthcoming low-frequency radio surveys. Our estimate is based on a FLASH simulation of the {Lambda}CDM model for which we have assigned radio power to clusters via a model that relates radio emissivity to cluster magnetic field strength, intracluster turbulence, and density. We vary several free parameters of this model and find that radio halo number counts vary by up to a factor of two for average magnetic fields ranging from 0.2 to 3.1 {mu}G. However, we predict significantly fewer low-frequency radio halos than expected from previous semi-analytic estimates, although this discrepancy could be explained by frequency-dependent radio halo probabilities as predicted in reacceleration models. We find that upcoming surveys will have difficulty in distinguishing models because of large uncertainties and low number counts. Additionally, according to our modeling we find that expected number counts can be degenerate with both reacceleration and hadronic secondary models of cosmic-ray generation. We find that relations between radio power and mass and X-ray luminosity may be used to distinguish models, and by building mock radio sky maps we demonstrate that surveys such as LOFAR may have sufficient resolution and sensitivity to break this model degeneracy by imaging many individual clusters.
79 ASTRONOMY AND ASTROPHYSICS↗
Time Evolving Fission Chain Theory and Fast Neutron and Gamma-Ray Counting Distributions
Here, we solve a simple theoretical model of time evolving fission chains due to Feynman that generalizes and asymptotically approaches the point model theory. The point model theory has been used to analyze thermal neutron counting data. This extension of the theory underlies fast counting data for both neutrons and gamma rays from metal systems. Fast neutron and gamma-ray counting is now possible using liquid scintillator arrays with nanosecond time resolution. For individual fission chains, the differential equations describing three correlated probability distributions are solved: the time-dependent internal neutron population, accumulation of fissions in time, and accumulation of leaked neutrons in time. Explicit analytic formulas are given for correlated moments of the time evolving chain populations. The equations for random time gate fast neutron and gamma-ray counting distributions, due to randomly initiated chains, are presented. Correlated moment equations are given for both random time gate and triggered time gate counting. There are explicit formulas for all correlated moments are given up to triple order, for all combinations of correlated fast neutrons and gamma rays. The nonlinear differential equations for probabilities for time dependent fission chain populations have a remarkably simple Monte Carlo realization. A Monte Carlo code was developed for this theory and is shown to statistically realize the solutions to the fission chain theory probability distributions. Combined with random initiation of chains and detection of external quanta, the Monte Carlo code generates time tagged data for neutron and gamma-ray counting and from these data the counting distributions.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Kinetics of photogenerated carbon dangling bonds in organic photovoltaic thin Films: An EPR study
Here, we report an investigation of the early kinetics of photogenerated carbon dangling bond (CDB) formation and annealing in organic photovoltaic bulk heterojunction (BHJ) thin film blends under oxygen- and moisture-free conditions, using X-band electron paramagnetic resonance (EPR) spectroscopy. The study focuses on donor:acceptor BHJ blends of PCE12:PCBM and PCE12:ITIC films, where PCE12 is PBDB-T. The time evolution of CDBs in such drop-cast BHJ films irradiated at 300 nm is monitored. The early kinetics of CDB formation, critical for understanding OPV degradation mechanisms, is studied. Theoretical analysis of the defect growth mechanism suggests a monomolecular defect creation model where the defect count follows a power-law t β with irradiation time t, where β ∼ 0.55–0.58, in excellent agreement with the theoretically expected value of β = 1/2. This model is compatible with CDB formation by the holes in donor sites adjacent to acceptors, likely assisted by energy released from quenching of nearby excitons by the holes, elucidating the physical mechanism underlying CDB formation. This is significant for designing improved materials, which mitigate defect creation, and consequently advancing the development of stable OPV systems.
42 ENGINEERING↗
Upgrading a high-throughput spectrometer for high-frequency (<400 kHz) measurements
The upgraded spectrometer used for charge exchange recombination spectroscopy on the Madison Symmetric Torus resolves emission fluctuations up to 400 kHz. The transimpedance amplifier’s cutoff frequency was increased based upon simulations comparing the change in the measured photon counts for time-dynamic signals. We modeled each signal-processing stage of the diagnostic and scanned the filtering frequency to quantify the uncertainty in the photon counting rate. This modeling showed that uncertainties can be calculated based on assuming each amplification stage is a Poisson process and by calibrating the photon counting rate with a DC light source to address additional variation.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Interstellar hydrogen fluxes measured by IBEX-LO in 2009: Numerical modeling and comparison with the data
In this paper, we perform numerical modeling of the interstellar hydrogen fluxes measured by IBEX-Lo during orbit 23 (spring 2009) using a state-of-the-art kinetic model of the interstellar neutral hydrogen distribution in the heliosphere. This model takes into account the temporal and heliolatitudinal variations of the solar parameters as well as the non-Maxwellian kinetic properties of the hydrogen distribution due to charge exchange in the heliospheric interface. We found that there is a qualitative difference between the IBEX-Lo data and the modeling results obtained with the three-dimensional, time-dependent model. Namely, the model predicts a larger count rate in energy bin 2 (20–41 eV) than in energy bin 1 (11–21 eV), while the data shows the opposite case. We perform study of the model parameter effects on the IBEX-Lo fluxes and the ratio of fluxes in two energy channels. We show that the most important parameter, which has a major influence on the ratio of the fluxes in the two energy bins, is the solar radiation pressure. The parameter fitting procedure shows that the best agreement between the model result and the data occurs when the ratio of the solar radiation pressure to the solar gravitation, μ{sub 0}, is 1.26{sub −0.076}{sup +0.06}, and the total ionization rate of hydrogen at 1 AU is β{sub E,0}=3.7{sub −0.35}{sup +0.39}×10{sup −7} s{sup −1}. We have found that the value of μ{sub 0} is much larger than μ{sub 0} = 0.89, which is the value derived from the integrated solar Lyα flux data for the period of time studied. We discuss possible reasons for the differences.
79 ASTRONOMY AND ASTROPHYSICS↗
Star counts from the Hubble Space Telescope Snapshot Survey. I - Galactic models
We report a photometric study of stars from 450 fields at high Galactic latitudes that were observed in the Hubble Space Telescope Snapshot Survey to an average limiting apparent magnitude of V = 21.4. There are 166 fields that contain quasars selected in radio, X-ray, and color-excess surveys. This sample of 273 stars is free of selection bias with respect to the density of stars. To within the Poisson errors, the total counts and magnitude distribution of this unbiased sample are in agreement with the Bahcall-Soneira model (Bahcall, 1986). The angular distribution of the faint stars favors, at the 2 sigma level, a somewhat steeper disk luminosity function and a smaller spheroid main-sequence normalization than given by the model. The sample does not have enough statistical power to distinguish between the two-component Bahcall-Soneira model and the three-component model first proposed by Gilmore and Reid (1983), which contains a thick disk. The statistical power of the survey would increase about 15-fold if colors were obtained for the stars: the data probe the main sequences of the disk, thick disk, and spheroid. Models with and without a thick disk could then be distinguished at the 6 sigma level. The HST Snapshot Survey includes an additional 284 fields, 279 of which are centered on quasars that were selected by objective-prism surveys. These 279 fields are expected to show and do exhibit bias against bright stars, making them unsuitable for testing Galactic models.
Gould, A.↗