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At least 667 records · Page 37
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NASA NTRS2000

Improved Results for Route Planning in Stochastic Transportation Networks

In the bus network problem, the goal is to generate a plan for getting from point X to point Y within a city using buses in the smallest expected time. Because bus arrival times are not determined by a fixed schedule but instead may be random. the problem requires more than standard shortest path techniques. In recent work, Datar and Ranade provide algorithms in the case where bus arrivals are assumed to be independent and exponentially distributed. We offer solutions to two important generalizations of the problem, answering open questions posed by Datar and Ranade. First, we provide a polynomial time algorithm for a much wider class of arrival distributions, namely those with increasing failure rate. This class includes not only exponential distributions but also uniform, normal, and gamma distributions. Second, in the case where bus arrival times are independent and geometric discrete random variable,. we provide an algorithm for transportation networks of buses and trains, where trains run according to a fixed schedule.

Boyan, Justin↗
DOE OSTI2017

Unraveling the Agglomeration Mechanism in Charged Block Copolymer and Surfactant Complexes

Here, we report a molecular dynamics simulation investigation of self-assembly and complex formation of charged-neutral double hydrophilic and hydrophobic-hydrophilic block copolymers (BCP) with oppositely charged surfactants. Furthermore, the structure of the surfactant micelles and the BCP aggregation on the micelle surface is systematically studied for five different BCP volume fractions that also mimics a reduction of the surfactant concentration. The local electrostatic interactions between the oppositely charged species encourage the formation of core-shell structures between the surfactant micelles where the surfactants form the cores and the charged blocks of the BCP form the corona. The emergent morphologies of these aggregates are contingent upon the nature of the BCP neutral blocks. The hydrophilic neutral blocks agglomerate with the micelles as hairy colloidal structures while the hydrophobic neutrals agglomerate in lamellar structures with the surfactant micelles. The distribution of counterion charges along the simulation box show a close-to-normal density distribution for the hydrophilic neutral blocks and a binodal distribution for hydrophobic neutral blocks. No specific surfactant concentration dependent scaling relation is observed as opposed to the simpler case of homo-polyelectrolytes.

36 MATERIALS SCIENCE↗
DOE OSTI2011

SUZAKU OBSERVATIONS OF THREE FeLoBAL QUASI-STELLAR OBJECTS: SDSS J0943+5417, J1352+4239, AND J1723+5553

We present Suzaku observations of three iron low-ionization broad absorption line quasars (FeLoBALs). We detect J1723+5553 (3{sigma}) in the observed 2-10 keV band, and constrain its intrinsic N{sub H} column density to N{sub H} > 6 x 10{sup 23} cm{sup -2} by modeling its X-ray hardness ratio. We study the broadband spectral index, {alpha}{sub ox}, between the X-ray and UV bands by combining the X-ray measurements and the UV flux extrapolated from Two Micron All Sky Survey magnitudes, assuming a range of intrinsic column densities, and then comparing the {alpha}{sub ox} values for the three FeLoBALs with those from a large sample of normal quasars. We find that the FeLoBALs are consistent with the spectral energy distribution of normal quasars if the intrinsic N{sub H} column densities are N{sub H} > 7 x 10{sup 23} cm{sup -2} for J0943+5417, N{sub H}>2 x 10{sup 24} cm{sup -2} for J1352+4293, and 6 x 10{sup 23} cm{sup -2} < N{sub H} < 3 x 10{sup 24} cm{sup -2} for J1723+5553. At these large intrinsic column densities, the optical depth from Thomson scattering can reach {approx}6, which will significantly modulate the UV flux. Our results suggest that the X-ray absorbing material could be located at a different place from the UV absorbing wind, likely between the X-ray and UV emitting regions. We find a significant kinetic feedback efficiency for FeLoBALs, indicating that the outflows are an important feedback mechanism in quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2000

The BATSE Gamma-Ray Burst E-Peak Distribution

Gamma-ray burst observed by BATSE are found to have approximately the same characteristic energy, denoted as E_p. We examine whether instrumental effects can give rise to this observation. We simulate the derivation of E-p and determine that the values in the BATSE sample are accurate and complete above a minimum fluence. We simulate the triggering of BATSE on gamma-ray bursts, deriving the efficiency of detecting bursts as a function of characteristic energy. From this simulation, we model the observed E_p distribution function expected when the intrinsic distribution function is a power-law. We find that this distribution produces poor fits to the observations. We find that a log-normal intrinsic distribution with a power-law tail gives a good fit to the data. From these fits, we conclude that instrumental effects cannot produce the observed E_p distribution, and that the observed distribution is a consequence of a narrow intrinsic distribution of E_p in gamma-ray bursts.

Brainerd, Jerome J.↗
DOE OSTI2025

The Multi-Phase Circumgalactic Medium of DESI Emission-Line Galaxies at z~1.5

We study the multi-phase circumgalactic medium (CGM) of emission line galaxies (ELGs) at $z\sim1.5$, traced by MgII$\lambda2796$, $\lambda2803$ and CIV$\lambda1548$, $\lambda1550$ absorption lines, using approximately 7,000 ELG-quasar pairs from the Dark Energy Spectroscopic Instrument. Our results show that both the mean rest equivalent width ($W_{0}$) profiles and covering fractions of MgII and CIV increase with ELG stellar mass at similar impact parameters, but show similar distributions when normalized by the virial radius. Moreover, warm CIV gas has a more extended distribution than cool MgII gas. The dispersion of MgII and CIV gas velocity offsets relative to the galaxy redshifts rises from $\sim100 \, \rm km \, s^{-1}$ within halos to $\sim 200 \, \rm km \, s^{-1}$ beyond. We explore the relationships between MgII and CIV $W_{0}$ and show that the two are not tightly coupled: at a fixed absorption strength of one species, the other varies by several-fold, indicating distinct kinematics between the gas phases traced by each. We measure the line ratios, FeII/MgII and CIV/MgII, of strong MgII absorbers and find that at $<0.2$ virial radius, the FeII/MgII ratio is elevated, while the CIV/MgII ratio is suppressed compared with the measurements on larger scales, both with $\sim4-5\, σ$ significance. We argue that multiphase gas that is not co-spatial is required to explain the observational results. Finally, by combining with measurements from the literature, we investigate the redshift evolution of CGM properties and estimate the neutral hydrogen, metal, and dust masses in the CGM of DESI ELGs -- found to be comparable to those in the ISM.

Lan, Ting-Wen [Taiwan, Natl. Taiwan U.; Academia S↗
DOE OSTI2024

A Pseudoreversible Normalizing Flow for Stochastic Dynamical Systems with Various Initial Distributions

Here, we present a pseudoreversible normalizing flow method for efficiently generating samples of the state of a stochastic differential equation (SDE) with various initial distributions. The primary objective is to construct an accurate and efficient sampler that can be used as a surrogate model for computationally expensive numerical integration of SDEs, such as those employed in particle simulation. After training, the normalizing flow model can directly generate samples of the SDE’s final state without simulating trajectories. The existing normalizing flow model for SDEs depends on the initial distribution, meaning the model needs to be retrained when the initial distribution changes. The main novelty of our normalizing flow model is that it can learn the conditional distribution of the state, i.e., the distribution of the final state conditional on any initial state, such that the model only needs to be trained once and the trained model can be used to handle various initial distributions. This feature can provide a significant computational saving in studies of how the final state varies with the initial distribution. Additionally, we propose to use a pseudoreversible network architecture to define the normalizing flow model, which has sufficient expressive power and training efficiency for a variety of SDEs in science and engineering, e.g., in particle physics. We provide a rigorous convergence analysis of the pseudoreversible normalizing flow model to the target probability density function in the Kullback–Leibler divergence metric. Numerical experiments are provided to demonstrate the effectiveness of the proposed normalizing flow model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Elliptically-Contoured Tensor-variate Distributions with Application to Image Learning

Statistical analysis of tensor-valued data has largely used the tensor-variate normal (TVN) distribution that may be inadequate for data arising from distributions with heavier or lighter tails. We study a general family of elliptically contoured (EC) TV distributions and derive its characterizations, moments, marginal, and conditional distributions. We describe procedures for maximum likelihood estimation from data that are (1) uncorrelated draws from an EC distribution, (2) from a scale mixture of the TVN distribution, and (3) from an underlying but unknown EC distribution, for which we extend Tyler’s robust estimator. A detailed simulation study highlights the benefits of choosing an EC distribution over the TVN for heavier-tailed data. We develop TV classification rules using discriminant analysis and EC errors and show that they better predict cats and dogs from images in the Animal Faces-HQ dataset than the TVN-based rules. A novel tensor-on-tensor regression and TV analysis of variance (TANOVA) framework under EC errors is also demonstrated to better characterize gender, age, and ethnic origin than the usual TVN-based TANOVA in the celebrated labeled faces of the wild dataset.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Neutrino mass and mass ordering: no conclusive evidence for normal ordering

The extraction of the neutrino mass ordering is one of the major challenges in particle physics and cosmology, not only for its implications for a fundamental theory of mass generation in nature, but also for its decisive role in the scale of future neutrinoless double beta decay experimental searches. It has been recently claimed that current oscillation, beta decay and cosmological limits on the different observables describing the neutrino mass parameter space provide robust decisive Bayesian evidence in favor of the normal ordering of the neutrino mass spectrum. We further investigate these strong claims using a rich and wide phenomenology, with different sampling techniques of the neutrino parameter space. Contrary to the findings of Jimenez et al., no decisive evidence for the normal mass ordering is found. Neutrino mass ordering analyses must rely on priors and parameterizations that are ordering-agnostic: robust results should be regarded as those in which the preference for the normal neutrino mass ordering is driven exclusively by the data, while we find a difference of up to a factor of 33 in the Bayes factors among the different priors and parameterizations exploited here. An ordering-agnostic prior would be represented by the case of parameterizations sampling over the two mass splittings and a mass scale, or those sampling over the individual neutrino masses via normal prior distributions only. In this regard, we show that the current significance in favor of the normal mass ordering should be taken as 2.7σ (i.e. moderate evidence), mostly driven by neutrino oscillation data. Let us stress that, while current data favor NO only mildly, we do not exclude the possibility that this may change in the future. Eventually, upcoming oscillation and cosmological data may (or may not) lead to a more significant exclusion of IO.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-G-TeP3-09: Proton Minibeam Radiation Therapy Increases Normal Tissue Resistance

Purpose: The dose tolerances of normal tissues continue being the main limitation in radiotherapy. To overcome it, we recently proposed a novel concept: proton minibeam radiation therapy (pMBRT) [1]. It allies the physical advantages of protons with the normal tissue preservation observed when irradiated with submillimetric spatially fractionated beams (minibeam radiation therapy) [2]. The dose distributions are such that the tumor receives a homogeneous dose distribution, while normal tissues benefit from the spatial fractionation of the dose. The objective of our work was to implement this promising technique at a clinical center (Proton therapy center in Orsay) in order to evaluate the potential gain in tissue sparing. Methods: Dose distributions were measured by means of gafchromic films and a PTW microdiamond detector (60019). Once the dosimetry was established, the whole brain of 7 weeks old male Fischer 344 rats was irradiated. Half of the animals received conventional seamless proton irradiation (25 Gy in one fraction). The other rats were irradiated with pMBRT (58 Gy peak dose in one fraction). The average dose deposited in the same targeted volume was in both cases 25 Gy. Results: The first complete set of dosimetric data in such small proton field sizes was obtained [3]. Rats treated with conventional proton irradiation exhibited severe moist desquamation and permanent epilation afterwards. The minibeam group, on the other hand, exhibited no skin damage and no clinical symptoms. MRI imaging and histological analysis are planned at 6 months after irradiation. Conclusion: Our preliminary results indicate that pMBRT leads to an increase in tissue resistance. This can open the door to an efficient treatment of very radioresistant tumours. [1] Prezado et al. Med. Phys. 40, 031712, 1–8 (2013).[2] Prezado et al., Rad. Research. 184, 314-21 (2015). [3] Peucelle et al., Med. Phys. 42 7108-13 (2015).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2010

Constructing and sampling graphs with a given joint degree distribution.

One of the most influential recent results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest understanding the relationship between network structure and the joint degree distribution of graphs is an interesting avenue of further research. An important tool for such studies are algorithms that can generate random instances of graphs with the same joint degree distribution. This is the main topic of this paper and we study the problem from both a theoretical and practical perspective. We provide an algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via end point switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge. These experiments show that our Markov Chain mixes quickly on real graphs, allowing for utilization of our techniques in practice.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

THE MASS DISTRIBUTION OF STELLAR-MASS BLACK HOLES

We perform a Bayesian analysis of the mass distribution of stellar-mass black holes using the observed masses of 15 low-mass X-ray binary systems undergoing Roche lobe overflow and 5 high-mass, wind-fed X-ray binary systems. Using Markov Chain Monte Carlo calculations, we model the mass distribution both parametrically-as a power law, exponential, Gaussian, combination of two Gaussians, or log-normal distribution-and non-parametrically-as histograms with varying numbers of bins. We provide confidence bounds on the shape of the mass distribution in the context of each model and compare the models with each other by calculating their relative Bayesian evidence as supported by the measurements, taking into account the number of degrees of freedom of each model. The mass distribution of the low-mass systems is best fit by a power law, while the distribution of the combined sample is best fit by the exponential model. This difference indicates that the low-mass subsample is not consistent with being drawn from the distribution of the combined population. We examine the existence of a 'gap' between the most massive neutron stars and the least massive black holes by considering the value, M{sub 1%}, of the 1% quantile from each black hole mass distribution as the lower bound of black hole masses. Our analysis generates posterior distributions for M{sub 1%}; the best model (the power law) fitted to the low-mass systems has a distribution of lower bounds with M{sub 1%}>4.3 M{sub sun} with 90% confidence, while the best model (the exponential) fitted to all 20 systems has M{sub 1%}>4.5 M{sub sun} with 90% confidence. We conclude that our sample of black hole masses provides strong evidence of a gap between the maximum neutron star mass and the lower bound on black hole masses. Our results on the low-mass sample are in qualitative agreement with those of Ozel et al., although our broad model selection analysis more reliably reveals the best-fit quantitative description of the underlying mass distribution. The results on the combined sample of low- and high-mass systems are in qualitative agreement with Fryer and Kalogera, although the presence of a mass gap remains theoretically unexplained.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-T-347: An Absolute Dose-Volume Constraint Based Deterministic Optimization Framework for Multi-Co60 Source Focused Radiotherapy

Purpose: Treatment plan optimization in multi-Co60 source focused radiotherapy with multiple isocenters is challenging, because dose distribution is normalized to maximum dose during optimization and evaluation. The objective functions are traditionally defined based on relative dosimetric distribution. This study presents an alternative absolute dose-volume constraint (ADC) based deterministic optimization framework (ADC-DOF). Methods: The initial isocenters are placed on the eroded target surface. Collimator size is chosen based on the area of 2D contour on corresponding axial slice. The isocenter spacing is determined by adjacent collimator sizes. The weights are optimized by minimizing the deviation from ADCs using the steepest descent technique. An iterative procedure is developed to reduce the number of isocenters, where the isocenter with lowest weight is removed without affecting plan quality. The ADC-DOF is compared with the genetic algorithm (GA) using the same arbitrary shaped target (254cc), with a 15mm margin ring structure representing normal tissues. Results: For ADC-DOF, the ADCs imposed on target and ring are (D100>10Gy, D50,10, 0<12Gy, 15Gy and 20Gy) and (D40<10Gy). The resulting D100, 50, 10, 0 and D40 are (9.9Gy, 12.0Gy, 14.1Gy and 16.2Gy) and (10.2Gy). The objectives of GA are to maximize 50% isodose target coverage (TC) while minimize the dose delivered to the ring structure, which results in 97% TC and 47.2% average dose in ring structure. For ADC-DOF (GA) techniques, 20 out of 38 (10 out of 12) initial isocenters are used in the final plan, and the computation time is 8.7s (412.2s) on an i5 computer. Conclusion: We have developed a new optimization technique using ADC and deterministic optimization. Compared with GA, ADC-DOF uses more isocenters but is faster and more robust, and achieves a better conformity. For future work, we will focus on developing a more effective mechanism for initial isocenter determination.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

INCLINATION-DEPENDENT ACTIVE GALACTIC NUCLEUS FLUX PROFILES FROM STRONG LENSING OF THE KERR SPACETIME

Recent quasar microlensing observations have constrained the X-ray emission sizes of quasars to be about 10 gravitational radii, one order of magnitude smaller than the optical emission sizes. Using a new ray-tracing code for the Kerr spacetime, we find that the observed X-ray flux is strongly influenced by the gravity field of the central black hole, even for observers at moderate inclination angles. We calculate inclination-dependent flux profiles of active galactic nuclei in the optical and X-ray bands by combining the Kerr lensing and projection effects for future reference. We further study the dependence of the X-ray-to-optical flux ratio on the inclination angle caused by differential lensing distortion of the X-ray and optical emission, assuming several corona geometries. The strong lensing X-ray-to-optical magnification ratio can change by a factor of {approx}10 for normal quasars in some cases, and a further factor of {approx}10 for broad absorption line (BAL) quasars and obscured quasars. Comparing our results with the observed distributions in normal and BAL quasars, we find that the inclination angle dependence of the magnification ratios can significantly change the X-ray-to-optical flux ratio distributions. In particular, the mean value of the spectrum slope parameter {alpha}{sub ox}, 0.3838log F {sub 2keV}/F {sub 2500A}, can differ by {approx}0.1-0.2 between normal and BAL quasars, depending on corona geometries, suggesting larger intrinsic absorptions in BAL quasars.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

Sampling graphs with a prescribed joint degree distribution using Markov Chains.

One of the most influential results in network analysis is that many natural networks exhibit a power-law or log-normal degree distribution. This has inspired numerous generative models that match this property. However, more recent work has shown that while these generative models do have the right degree distribution, they are not good models for real life networks due to their differences on other important metrics like conductance. We believe this is, in part, because many of these real-world networks have very different joint degree distributions, i.e. the probability that a randomly selected edge will be between nodes of degree k and l. Assortativity is a sufficient statistic of the joint degree distribution, and it has been previously noted that social networks tend to be assortative, while biological and technological networks tend to be disassortative. We suggest that the joint degree distribution of graphs is an interesting avenue of study for further research into network structure. We provide a simple greedy algorithm for constructing simple graphs from a given joint degree distribution, and a Monte Carlo Markov Chain method for sampling them. We also show that the state space of simple graphs with a fixed degree distribution is connected via endpoint switches. We empirically evaluate the mixing time of this Markov Chain by using experiments based on the autocorrelation of each edge.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

THE INITIAL MASS FUNCTION MODELED BY A LEFT TRUNCATED BETA DISTRIBUTION

The initial mass function for stars is usually fitted by three straight lines, which means it has seven parameters. The presence of brown dwarfs (BDs) increases the number of straight lines to four and the number of parameters to nine. Another common fitting function is the lognormal distribution, which is characterized by two parameters. This paper is devoted to demonstrating the advantage of introducing a left truncated beta probability density function, which is characterized by four parameters. The constant of normalization, the mean, the mode, and the distribution function are calculated for the left truncated beta distribution. The normal beta distribution that results from convolving independent normally distributed and beta distributed components is also derived. The chi-square test and the Kolmogorov-Smirnov test are performed on a first sample of stars and BDs that belongs to the massive young cluster NGC 6611, and on a second sample that represents the masses of the stars of the cluster NGC 2362.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1972

Fourier spectrum of beams chopped by circular apertures.

The circular apertures in the case investigated are uniformly spaced. It is assumed that modulation does not alter beam collimation and that diffraction from aperture edges can be neglected. It is also assumed that the diameter of the apertures is equal to the spacing between the trailing edge and the leading edge of successive apertures. The motion of apertures across the beam is rectilinear. Calculations have been performed for both homogeneous and bivariate normal intensity distributions. The absolute values of the first six Fourier coefficients are plotted as a function of the beam radius normalized to aperture radius.

Cohen, S. C.↗
NASA NTRS1986

The frequency spectrum of C sub n sup 2 from MST radar data

In a recent study (Nastrom et al., 1986), the variability of the refractivity turbulence structure constant, C sub n(2), was examined using observations from the stratosphere troposphere/mesosphere stratosphere troposphere (ST/MST) radar at Poker Flat, Alaska, and Platteville, Colorado. Variations of C2 with height, season, and weather conditions were examined. Also, the autocorrelation function and the frequency distribution of C sub n(2) were studied, and it was shown that C sub n(2), follows a log-normal frequency distribution. One of the more tentative results given in that paper is a first look at the spectrum of log C sub n(2), as a function of frequency at Poker Flat. This spectrum appears to obey a power law relation with frequency, P(F) approx. F(k), with k near -5/3 at periods between about 4 hours and 6 days, and with k near -1 at shorter periods. Power law behavior of a spectrum often helps us to infer the underlying dynamics which give rise to this spectrum, and it is thus of some concern to establish further confidence in the spectral shape. The purpose here is to address these questions.

Nastrom, G. D.↗