Dijet production in s = 7 TeV pp collisions with large rapidity gaps at the ATLAS experiment
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
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Results for “Probability Distribution Function”
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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Direct numerical simulations of three-component Rayleigh–Taylor mixing and an improved model for multicomponent reacting mixtures
We present direct numerical simulations of a three-layer Rayleigh–Taylor instability (RTI) problem with a configuration based on the experiments of Suchandra & Ranjan ( J. Fluid Mech. , vol. 974, 2023, A35) and Jacobs & Dalziel ( J. Fluid Mech. , vol. 542, 2005, pp. 251–279). The problem consists of a layer of light fluid between two layers of heavy fluid with an Atwood number of 0.3. These simulations are first validated through comparison with available experimental data. The validated simulations are then utilized to analyse statistics in this three-component flow. First, length scales are examined utilizing spectra and two-point spatial correlations of velocity and species concentration fluctuations. Next, joint probability density functions (p.d.f.s) of species concentration are compared against several model p.d.f.s representing generalizations of the bivariate beta distribution. Notably, the joint p.d.f.s do not appear to be accurately described by a Dirichlet distribution, indicating the marginal distributions do not conform to a beta distribution. Finally, similarity of the present configuration to three-component mixing found in inertial confinement fusion (ICF) applications is exploited to develop and validate an improved model for the impact of multicomponent mixing on thermonuclear (TN) reaction rates. A single time instant from the present simulations is chosen for a TN burn calculation under the hypothetical assumption of ICF materials and temperatures. Total TN output from this second calculation is then compared against the prediction of the improved model. The new model is found to accurately predict TN reaction rates in both premixed and non-premixed configurations.
42 ENGINEERING↗
Electron transport in discontinuous gold films and the effect of Coulomb blockade and percolation
Understanding the electron transport in disordered assemblies of weakly coupled nano-sized metal clusters is important for many applications. Here, we investigate experimentally and theoretically the electron transport properties of metal cluster assemblies in the form of discontinuous gold films. Discontinuous films of different average island size are produced by sputter deposition, and the resistance and the non-linear current-voltage (I-V) characteristics of the films are measured as a function of temperature. To interpret the experimental electron transport data, a conduction percolation model is employed where broad probability distributions for both the tunnel junction gaps and the Coulomb blockade energies are used. Excellent agreement between experimental data and model calculations is found. In particular, the non-Arrhenius resistive behavior, the I-V power-law behavior, and the I-V characteristics at large bias voltage are all shown to be due to a conduction percolation mechanism governing disordered networks of nano-sized metal islands connected by tunnel junctions.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Letter: Modeling reactive shock waves in heterogeneous solids at the continuum level with stochastic differential equations
In this paper, a new paradigm is introduced for modeling reactive shock waves in heterogeneous solids at the continuum level. Inspired by the probability density function methods from turbulent reactive flows, it is hypothesized that the unreacted material microstructures lead to a distribution of heat release rates from chemical reaction. Fluctuations in heat release, rather than velocity, are coupled to the reactive Euler equations which are then solved via the Riemann problem. Finally, a numerically efficient, one-dimensional hydrocode is used to demonstrate this new approach, and simulation results of a representative impact calculation (inert flyer into explosive target) are discussed.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Influence of hydrostatic pressure on hidden order, the Kondo lattice, and magnetism in URu 2 Si 2-x P x
Within the chemical substitution series URu 2 Si 2-x P x , there is an evolution in the ground-state behavior from hidden ordered (HO) for x ≲ 0.03, to Kondo lattice behavior with no ordering (NO) for 0.03 ≲ x ≲ 0.26, to antiferromagnetism (AFM-2) for 0.26 ≲ x ≲ 0.5 [A. Gallagher et al., Nat. Commun. 7, 10712 (2016); A. Gallagher et al., J. Phys.: Condens. Matter 29, 024004 (2016)]. To better understand what factors control this behavior, temperature-dependent electrical resistivity measurements are performed for this series under applied pressures P up to 20.5 kbar. Specimens in the HO x region show similarities to the parent compound, where HO transforms into antiferromagnetism (AFM-1) at a critical pressure (P c ). P c decreases with increasing x and collapses towards P = 0 near x ≈ 0.03, suggesting that AFM-1 occurs at ambient pressure for this concentration. No pressure-induced phase transitions are observed in the NO x region and the AFM-2 state is only weakly suppressed by P. Measurements further reveal that AFM-1 and AFM-2 are distinct from each other. Calculations of the wave functions using the tight-binding Hartree-Fock approximation are performed and show (i) that the radial probability distributions for the phosphorus ions are more tightly bound than those for the silicon and (ii) that the energy difference between the orbitals decreases with increasing x. The cumulative effect of these two factors is that Si → P substitution decreases the hybridization strength, which correlates with the weakening of HO. At large x, additional effects such as electrical charge tuning also play an important role in determining the ground-state
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Quantum Random Walk Simulator Using Ultrafast Optical Switches
Quantum random walk processes have many intriguing applications in high energy physics including the simulation of parton shower evolution. We will present the design and initial results of a fiber loop time-bin quantum walk architecture using the hardware platform already in operation at the Fermilab Quantum Network in which the state of the photon is defined by its time-of-arrival. The fiber loop consists of an unbalanced Mach-Zehnder interferometer implemented using an ultrafast electro-optical switch. The input switch controls the photon path within the interferometer, while the output switch will direct the photon back into the interferometer or to single photon detectors to measure the probability distribution of arrival times. Depending on which path the photon takes each pass through the loop, its wave function will interfere on these optical switches similar to quantum interference on a beam splitter. This work is an important step towards utilizing real-world advantages of quantum information protocols to solve problems in high energy physics.
Cameron, Andrew [Fermilab]↗
Probabilistic assessment of cloud fraction using Bayesian blending of independent datasets: Feasibility study of a new method
We describe and evaluate a novel method to blend two observed cloud fraction (CF) datasets through Bayesian posterior estimation. The research reported here is a feasibility study designed to explore the method. In this proof‐of‐concept study, we illustrate the approach using specific observational datasets from the U. S. Department of Energy Atmospheric Radiation Measurement Program's Southern Great Plains site in the central United States, but the method is quite general and is readily applicable to other datasets. The total sky image (TSI) camera observations are used to determine the prior distribution. A regression model and the active remote sensing of clouds (ARSCL) radar/lidar observations are used to determine the likelihood function. The posterior estimate is a probability density function (pdf) of the CF whose mean is taken to be the optimal blend of the two observations. The data at hourly, daily, 5‐day, monthly, and annual time scales are considered. Some physical and probabilistic properties of the CFs are explored from radar/lidar, camera, and satellite observations and from simulations using the Community Atmosphere Model (CAM5). Our results imply that (a) the Beta distribution is a reasonable model for CF for both short‐ and long‐time means, the 5‐day data are skewed right, and the annual data are almost normally distributed, and (b) the Bayesian method developed successfully yields a pdf of CF, rather than a deterministic CF value, and it is feasible to blend the TSI and ARSCL data with a capability for bias correction.
58 GEOSCIENCES↗
The study of disorder and nanocrystallinity in C-S-H, supplementary cementitious materials and geopolymers using pair distribution function analysis
Significant progress was achieved with the application of Rietveld method to characterize the crystalline phases in portland cement paste. However, to obtain detailed information on the amorphous or poorly crystalline phases, it is necessary to analyze the total scattering data. The pair distribution function (PDF) method has been successfully used in the study of liquids and amorphous solids. The method takes the Sine Fourier transform of the measured structure factor over a wide momentum transfer range, providing a direct measure of the probability of finding an atom surrounding a central atom at a radial distance away. The obtained experimental characteristic distances can be also used to validate the predictions by the theoretical models, such as, molecular dynamics, ab initio simulations and density functional theory. The paper summarizes recent results of PDF analysis on silica fume, rice husk ash, fly ash, ASR gel, C-S-H and geopolymers.
36 MATERIALS SCIENCE↗
Failure-probability driven dose painting
Purpose: To demonstrate a data-driven dose-painting strategy based on the spatial distribution of recurrences in previously treated patients. The result is a quantitative way to define a dose prescription function, optimizing the predicted local control at constant treatment intensity. A dose planning study using the optimized dose prescription in 20 patients is performed.Methods: Patients treated at our center have five tumor subvolumes from the center of the tumor (PET positive volume) and out delineated. The spatial distribution of 48 failures in patients with complete clinical response after (chemo)radiation is used to derive a model for tumor control probability (TCP). The total TCP is fixed to the clinically observed 70% actuarial TCP at five years. Additionally, the authors match the distribution of failures between the five subvolumes to the observed distribution. The steepness of the dose–response is extracted from the literature and the authors assume 30% and 20% risk of subclinical involvement in the elective volumes. The result is a five-compartment dose response model matching the observed distribution of failures. The model is used to optimize the distribution of dose in individual patients, while keeping the treatment intensity constant and the maximum prescribed dose below 85 Gy.Results: The vast majority of failures occur centrally despite the small volumes of the central regions. Thus, optimizing the dose prescription yields higher doses to the central target volumes and lower doses to the elective volumes. The dose planning study shows that the modified prescription is clinically feasible. The optimized TCP is 89% (range: 82%–91%) as compared to the observed TCP of 70%.Conclusions: The observed distribution of locoregional failures was used to derive an objective, data-driven dose prescription function. The optimized dose is predicted to result in a substantial increase in local control without increasing the predicted risk of toxicity.
61 RADIATION PROTECTION AND DOSIMETRY↗
Application of machine learning for the estimation of electron energy distribution from optical emission spectra
Abstract This paper discusses the use of probabilistic deep neural networks for the prediction of the electron energy probability function in low-temperature non-thermal plasmas. The neural networks are trained using optical emission spectroscopy and Langmuir probe measurements, with the goal of providing a reliable estimate of the electron energy probability function solely from optical emission data. The performance of both non-Bayesian and Bayesian networks is evaluated. It is found that Bayesian models are preferable as they assign a higher level of uncertainty to their prediction especially when the dataset used to train them is small. This work describes one of the many potential applications of machine learning in plasma science and technology.
Physics↗
Gender differences in cadmium and cotinine levels in prepubertal children
Susceptibility to environmental stressors has been described for fetal and early childhood development. However, the possible susceptibility of the prepubertal period, characterized by the orchestration of the organism towards sexual maturation and adulthood has been poorly investigated and exposure data are scarce. In the current study levels of cadmium (Cd), cotinine and creatinine in urine were analyzed in a subsample 216 children from 12 European countries within the DEMOCOPHES project. The children were divided into six age–sex groups: boys (6–8 years, 9–10 years and 11 years old), and girls (6–7 years, 8–9 years, 10–11 years). The number of subjects per group was between 23 and 53. The cut off values were set at 0.1 µg/L for Cd, and 0.8 µg/L for cotinine defined according to the highest limit of quantification. The levels of Cd and cotinine were adjusted for creatinine level. In the total subsample group, the median level of Cd was 0.180 µg/L (range 0.10–0.69 µg/L), and for cotinine the median wet weight value was 1.50 µg/L (range 0.80–39.91 µg/L). There was no significant difference in creatinine and cotinine levels between genders and age groups. There was a significant correlation between levels of cadmium and creatinine in all children of both genders. This shows that even at such low levels the possible effect of cadmium on kidney function was present and measurable. An increase in Cd levels was evident with age. Cadmium levels were significantly different between 6–7 year old girls, 11 year old boys and 10–11 year old girls. As there was a balanced distribution in the number of subjects from countries included in the study, bias due to data clustering was not probable. The impact of low Cd levels on kidney function and gender differences in Cd levels needs further investigation. - Highlights: • In 216 children from 6 to 11 years old the median level of Cd was 0.18 µg/L. • The median level of cotinine was 1.50 µg/L. • Correlation between levels of Cd and creatinine in all children was detected. • Cd levels were different between 6–7 year old, 10–11 year old girls vs. 11 year old boys.
54 ENVIRONMENTAL SCIENCES↗
A PLATEAU IN THE PLANET POPULATION BELOW TWICE THE SIZE OF EARTH
We carry out an independent search of Kepler photometry for small transiting planets with sizes 0.5-8.0 times that of Earth and orbital periods between 5 and 50 days, with the goal of measuring the fraction of stars harboring such planets. We use a new transit search algorithm, TERRA, optimized to detect small planets around photometrically quiet stars. We restrict our stellar sample to include the 12,000 stars having the lowest photometric noise in the Kepler survey, thereby maximizing the detectability of Earth-size planets. We report 129 planet candidates having radii less than 6 R{sub E} found in three years of Kepler photometry (quarters 1-12). Forty-seven of these candidates are not in Batalha et al., which only analyzed photometry from quarters 1-6. We gather Keck HIRES spectra for the majority of these targets leading to precise stellar radii and hence precise planet radii. We make a detailed measurement of the completeness of our planet search. We inject synthetic dimmings from mock transiting planets into the actual Kepler photometry. We then analyze that injected photometry with our TERRA pipeline to assess our detection completeness for planets of different sizes and orbital periods. We compute the occurrence of planets as a function of planet radius and period, correcting for the detection completeness as well as the geometric probability of transit, R{sub *}/a. The resulting distribution of planet sizes exhibits a power law rise in occurrence from 5.7 R{sub E} down to 2 R{sub E} , as found in Howard et al. That rise clearly ends at 2 R{sub E} . The occurrence of planets is consistent with constant from 2 R{sub E} toward 1 R{sub E} . This unexpected plateau in planet occurrence at 2 R{sub E} suggests distinct planet formation processes for planets above and below 2 R{sub E} . We find that 15.1{sup +1.8}{sub -2.7}% of solar type stars-roughly one in six-has a 1-2 R{sub E} planet with P = 5-50 days.
79 ASTRONOMY AND ASTROPHYSICS↗
MINIMUM REQUIREMENTS FOR DETECTING A STOCHASTIC GRAVITATIONAL WAVE BACKGROUND USING PULSARS
We assess the detectability of a nanohertz gravitational wave (GW) background in a pulsar timing array (PTA) program by considering the shape and amplitude of the cross-correlation function summed over pulsar pairs. The distribution of correlation amplitudes is found to be non-Gaussian and highly skewed, which significantly influences detection and false-alarm probabilities. When only white noise combines with GWs in timing data, our detection results are consistent with those found by others. Contamination by red noise from spin variations and from any uncorrected interstellar plasma effects significantly increases the false-alarm probability. The number of arrival times (and thus the observing cadence) is important only as long as the residuals are dominated by white noise. When red noise and GWs dominate, the statistical significance of the correlation estimate can be improved only by increasing the number of pulsars. We characterize plausible detection regimes by evaluating the number of millisecond pulsars (MSPs) that must be monitored in a high-cadence, five-year timing program to detect a GW background spectrum h{sub c} (f) = A(f/f{sub 0}){sup -2/3} with f{sub 0} = 1 yr{sup -1} and A = 10{sup -15}. Our results indicate that a sample of 20 super-stable MSPs-those with rms timing residuals {sigma}{sub r} {approx}< 20 ns(A/10{sup -15}) from red-noise contributions over a five-year span-will allow detection of the GW background and study of its spectrum. However, a timing program on {approx}> 50-100 MSPs is likely needed for a complete PTA program, particularly if red noise is generally present in MSPs.
79 ASTRONOMY AND ASTROPHYSICS↗
RECOILING SUPERMASSIVE BLACK HOLES IN SPIN-FLIP RADIO GALAXIES
Numerical relativity simulations predict that coalescence of supermassive black hole (SMBH) binaries leads not only to a spin flip but also to a recoiling of the merger remnant SMBHs. In the literature, X-shaped radio sources are popularly suggested to be candidates for SMBH mergers with spin flip of jet-ejecting SMBHs. Here we investigate the spectral and spatial observational signatures of the recoiling SMBHs in radio sources undergoing black hole spin flip. Our results show that SMBHs in most spin-flip radio sources have mass ratio q {approx}> 0.3 with a minimum possible value q{sub min} {approx_equal} 0.05. For major mergers, the remnant SMBHs can get a kick velocity as high as 2100 km s{sup -1} in the direction within an angle {approx}< 40 Degree-Sign relative to the spin axes of remnant SMBHs, implying that recoiling quasars are biased to be with high Doppler-shifted broad emission lines while recoiling radio galaxies are biased to large apparent spatial off-center displacements. We also calculate the distribution functions of line-of-sight velocity and apparent spatial off-center displacements for spin-flip radio sources with different apparent jet reorientation angles. Our results show that the larger the apparent jet reorientation angle is, the larger the Doppler-shifting recoiling velocity and apparent spatial off-center displacement will be. We investigate the effects of recoiling velocity on the dust torus in spin-flip radio sources and suggest that recoiling of SMBHs would lead to 'dust-poor' active galactic nuclei. Finally, we collect a sample of 19 X-shaped radio objects and for each object give the probability of detecting the predicted signatures of recoiling SMBH.
79 ASTRONOMY AND ASTROPHYSICS↗
Fast HARDI Uncertainty Quantification and Visualization with Spherical Sampling
In this paper, we study uncertainty quantification and visualization of orientation distribution functions (ODF), which corresponds to the diffusion profile of high angular resolution diffusion imaging (HARDI) data. The shape inclusion probability (SIP) function is the state‐of‐the‐art method for capturing the uncertainty of ODF ensembles. The current method of computing the SIP function with a volumetric basis exhibits high computational and memory costs, which can be a bottleneck to integrating uncertainty into HARDI visualization techniques and tools. We propose a novel spherical sampling framework for faster computation of the SIP function with lower memory usage and increased accuracy. In particular, we propose direct extraction of SIP isosurfaces, which represent confidence intervals indicating spatial uncertainty of HARDI glyphs, by performing spherical sampling of ODFs. Our spherical sampling approach requires much less sampling than the state‐of‐the‐art volume sampling method, thus providing significantly enhanced performance, scalability, and the ability to perform implicit ray tracing. Our experiments demonstrate that the SIP isosurfaces extracted with our spherical sampling approach can achieve up to 8164× speedup, 37282× memory reduction, and 50.2% less SIP isosurface error compared to the classical volume sampling approach. We demonstrate the efficacy of our methods through experiments on synthetic and human‐brain HARDI datasets.
97 MATHEMATICS AND COMPUTING↗
Multiclass Classification Using Bayesian Multivariate Adaptive Regression Splines
We present a new Bayesian model for the problem of multiclass classification. In this model, the probabilities of class membership of a given observation are determined by the mean of a latent Gaussian distribution. The mean functions of this latent distribution consist of combinations of highly flexible basis functions of the inputs: multivariate adaptive regression splines (MARS), first developed for multiple regression. We use reversible jump Markov chain Monte Carlo to make inference on the classification model, including the number of basis functions. We compare the probabilistic classification performance of our proposed approach to existing methods on simulated and benchmark data, and compare uncertainty estimates on simulated data. Our proposed method compares favorably with existing Bayesian and frequentist multiclass classification methods in out-of-sample probabilistic classification, and uncertainty estimation of these probabilistic classifications. We examine the fit of the proposed method to a data set of hurricane storm surge levels near Delaware Bay, US, and conclude that sea level rise is a key contributor to damage delivered by storm surge.
97 MATHEMATICS AND COMPUTING↗
A Novel Statistical Method for Measuring the Temperature–Density Relation in the IGM Using the b – N Hi Distribution of Absorbers in the Ly α Forest
We propose a new method for determining the thermal state of the intergalactic medium based on Voigt profile decomposition of the Lyα forest. The distribution of Doppler parameter and column density (b–N H i distribution) is sensitive to the temperature–density relation T = T 0 (ρ/ρ 0 ) γ-1 , and previous work has inferred T 0 and γ by fitting its low-b cutoff. This approach discards the majority of available data and is susceptible to systematics related to cutoff determination. We introduce an approach that exploits all information encoded in the b – N H i distribution by modeling its entire shape. We apply kernel density estimation to discrete absorption lines to generate model probability density functions, and then we use principal component decomposition to create an emulator that can be evaluated anywhere in thermal parameter space. We introduce a Bayesian likelihood based on these models enabling parameter inference via Markov Chain Monte Carlo. The method's robustness is tested by applying it to a large grid of thermal history simulations. By conducting 160 mock measurements, we establish that our approach delivers unbiased estimates and valid uncertainties for a 2D ( T 0 , γ) measurement. Furthermore, we conduct a pilot study applying this methodology to real observational data at z = 2. Using 200 absorbers, equivalent in path length to a single Lya forest spectrum, we measure $\mathrm{log}{T}_{0}={4.092}_{-0.055}^{+0.050}$ and $\gamma ={1.49}_{-0.074}^{+0.073}$ in excellent agreement with cutoff fitting determinations using the same data. Our method is far more sensitive than cutoff fitting, enabling measurements of log T 0 and γ with precision on $\mathrm{log}{T}_{0}$ (γ) nearly two (three) times higher for current data set sizes.
79 ASTRONOMY AND ASTROPHYSICS↗
Computing the moments of the neutron population using deterministic neutron transport
It is important to treat the inherent stochasticity of the fission process in systems where the behavior of the system is stochastic. This occurs when there are few neutrons in the system, or when the neutron source is weak. In order to characterize such systems, the capability to compute the first four moments of the neutron population distribution has been added to the deterministic neutral particle transport code, PARTISN. The moments are then fitted to probability density functions from the Pearson family. PARTISN is compared against MCNP6, with which it agrees well. (authors)
97 MATHEMATICS AND COMPUTING↗