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At least 775 records · Page 43

Hidden Statistics of Schroedinger Equation

Work was carried out in determination of the mathematical origin of randomness in quantum mechanics and creating a hidden statistics of Schr dinger equation; i.e., to expose the transitional stochastic process as a "bridge" to the quantum world. The governing equations of hidden statistics would preserve such properties of quantum physics as superposition, entanglement, and direct-product decomposability while allowing one to measure its state variables using classical methods.

Zak, Michail↗

Using Multi-Scale Modeling Systems and Satellite Data to Study the Precipitation Processes

In recent years, exponentially increasing computer power has extended Cloud Resolving Model (CRM) integrations from hours to months, the number of computational grid points from less than a thousand to close to ten million. Three-dimensional models are now more prevalent. Much attention is devoted to precipitating cloud systems where the crucial 1-km scales are resolved in horizontal domains as large as 10,000 km in two-dimensions, and 1,000 x 1,000 km2 in three-dimensions. Cloud resolving models now provide statistical information useful for developing more realistic physically based parameterizations for climate models and numerical weather prediction models. It is also expected that NWP and mesoscale model can be run in grid size similar to cloud resolving model through nesting technique. Recently, a multi-scale modeling system with unified physics was developed at NASA Goddard. It consists of (l) a cloud-resolving model (Goddard Cumulus Ensemble model, GCE model), (2) a regional scale model (a NASA unified weather research and forecast, WRF), (3) a coupled CRM and global model (Goddard Multi-scale Modeling Framework, MMF), and (4) a land modeling system. The same microphysical processes, long and short wave radiative transfer and land processes and the explicit cloud-radiation, and cloud-land surface interactive processes are applied in this multi-scale modeling system. This modeling system has been coupled with a multi-satellite simulator to use NASA high-resolution satellite data to identify the strengths and weaknesses of cloud and precipitation processes simulated by the model. In this talk, the recent developments and applications of the multi-scale modeling system will be presented. In particular, the results from using multi-scale modeling system to study the precipitating systems and hurricanes/typhoons will be presented. The high-resolution spatial and temporal visualization will be utilized to show the evolution of precipitation processes. Also how to use of the multi-satellite simulator tqimproy~ precipitation processes will be discussed.

Tao, Wei-Kuo↗

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, approximately 70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.

MAIAC AO↗

Multispectral Imagery Research and Applications

The NASA Short-term Prediction Research and Transition (SPoRT) Center developed techniques to improve the quality and interpretation of multispectral imagery derived from NASA/NOAA geostationary satellites using statistical and machine learning approaches. A physically-based machine learning approach, DustTracker-AI, was developed to overcome the problem of night-time dust detection and to augment dust analysis with satellite products such as the Dust RGB. Additionally, an updated limb-correction and intercalibration methodology for short-wave, near-infrared, thermal infrared, and water vapor bands was developed for the purpose of developing a suite of high-quality RGB imagery that can be used at high viewing angles and across the constellation of geostationary sensors. This presentation will briefly highlight the techniques developed to detect dust in difficult night-time scenes and improve the quality and interpretation of multispectral imagery.

Emily Berndt↗

A histogram-free multicanonical Monte Carlo algorithm for the construction of analytical density of states

We report a new multicanonical Monte Carlo (MC) algorithm to obtain the density of states (DOS) for physical systems with continuous state variables in statistical mechanics. Our algorithm is able to obtain an analytical form for the DOS expressed in a chosen basis set, instead of a numerical array of finite resolution as in previous variants of this class of MC methods such as the multicanonical (MUCA) sampling and Wang-Landau (WL) sampling. This is enabled by storing the visited states directly in a data set and avoiding the explicit collection of a histogram. This practice also has the advantage of avoiding undesirable artificial errors caused by the discretization and binning of continuous state variables. Our results show that this scheme is capable of obtaining converged results with a much reduced number of Monte Carlo steps, leading to a significant speedup over existing algorithms.

Li, Ying Wai↗

The Muon g-2 Straw Tracking Detectors

• Built to measure the muon anomalous magnetic moment to an uncertainty of 140 ppb and search for a muon electric dipole moment. • Determine whether discrepancy from the Standard Model in the previous experiment $\Delta 𝒂_{\mu}$ 𝑬𝟖𝟐𝟏 − 𝑺𝑴 = 𝟐𝟓𝟓 ± 𝟖𝟎 ×$𝟏𝟎^{-𝟏𝟏}$ is a statistical fluctuation or indicates non-SM physics. • A muon storage ring located at Fermilab, Illinois. • 15m diameter storage ring. • 1.45T uniform magnetic field. • 24 calorimeters around the ring. • 2 tracking stations. • Storing 100s of billions of muons.

43 PARTICLE ACCELERATORS↗

An Energy Calculator for Simple Commercial Buildings

According to the EIA, simple commercial buildings account for 97% of total commercial building stock. However, most simple commercial buildings for example small- to mid-sized offices, retail, schools and warehouses do not benefit from the data-driven decision-making capabilities of whole-building energy modeling. The high cost of custom modeling limits the use of energy modeling of simple buildings for new construction or retrofit measures. Lack of tools providing helpful information on interactive savings estimates creates difficulties in meeting aggressive decarbonization and energy efficiency goals for simple building designers and utility program managers. This paper reviews a beta phase Simple Building Calculator with the ability to generate relatively accurate and interactive modeling results based on a limited but robust set of inputs. It can evaluate whole-building or single measure savings in new or existing buildings, compare measure package choices, or provide simplified performance modeling for energy codes and utility incentives. The tool combines physical (annual whole building prototype simulation) and statistical modeling techniques to predict annual energy performance. It supports a variety of building characteristics for envelope, HVAC, and lighting with parameters ranging from vintage to max tech configurations, as well as support for single-zone and simple multi-zone HVAC systems. The Simple Building Calculator was designed to provide immediate feedback for otherwise computationally intensive tasks like measure comparison, development of multiple measure package combinations, or verification that measures meet efficiency targets—all with the goal of providing a tool for quick annual energy simulation of simple commercial buildings.

Hart, Reid↗

Realizable hyperuniform and nonhyperuniform particle configurations with targeted spectral functions via effective pair interactions

The capacity to identify realizable many-body configurations associated with targeted functional forms for the pair correlation function $g_2(r)$ or its corresponding structure factor $\textit{S(k)}$ is of great fundamental and practical importance. While there are obvious necessary conditions that a prescribed structure factor at number density ρ must satisfy to be configurationally realizable, sufficient conditions are generally not known due to the infinite degeneracy of configurations with different higher-order correlation functions. A major aim of this paper is to expand our theoretical knowledge of the class of pair correlation functions or structure factors that are realizable by classical disordered ensembles of particle configurations, including exotic “hyperuniform” varieties. We first introduce a theoretical formalism that provides a means to draw classical particle configurations from canonical ensembles with certain pairwise-additive potentials that could correspond to targeted analytical functional forms for the structure factor. This formulation enables us to devise an improved algorithm to construct systematically canonical-ensemble particle configurations with such targeted pair statistics, whenever realizable. As a proof of concept, we test the algorithm by targeting several different structure factors across dimensions that are known to be realizable and one hyperuniform target that is known to be nontrivially unrealizable. Our algorithm succeeds for all realizable targets and appropriately fails for the unrealizable target, demonstrating the accuracy and power of the method to numerically investigate the realizability problem. Subsequently, we also target several families of structure-factor functions that meet the known necessary realizability conditions but are not known to be realizable by disordered hyperuniform point configurations, including $\textit{d}$-dimensional Gaussian structure factors, $\textit{d}$-dimensional generalizations of the two-dimensional one-component plasma (OCP), and the $\textit{d}$-dimensional Fourier duals of the previous OCP cases. Moreover, we also explore unusual nonhyperuniform targets, including “hyposurficial” and “antihyperuniform” examples. In all of these instances, the targeted structure factors are achieved with high accuracy, suggesting that they are indeed realizable by equilibrium configurations with pairwise interactions at positive temperatures. Remarkably, we also show that the structure factor of nonequilibrium perfect glass, specified by two-, three-, and four-body interactions, can also be realized by equilibrium pair interactions at positive temperatures. In this work, our findings lead us to the conjecture that any realizable structure factor corresponding to either a translationally invariant equilibrium or nonequilibrium system can be attained by an equilibrium ensemble involving only effective pair interactions. Our investigation not only broadens our knowledge of analytical functional forms for $g_2(r)$ and $\textit{S(k)}$ associated with disordered point configurations across dimensions but also deepens our understanding of many-body physics. Moreover, our work can be applied to the design of materials with desirable physical properties that can be tuned by their pair statistics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Big data health physics: a case study in operational health physics

This paper, written as a two part project as required by Illinois Institute of Technology’s Phys597 Reading & Special Problems class and health physics professional master’s program, focuses on the results of a prototype data collection process implemented at a Los Alamos National Laboratory facility. In particular, this paper examines the data in a statistical context in an effort to optimize operational health physics processes at the Los Alamos National Laboratory.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Modeling Flow and Transport in Fractured Rock using Machine Learning [Slides]

Fractured systems in the subsurface play a role in many natural and engineered applications such as geologic carbon sequestration, hydraulic fracturing and underground nuclear test detection. Structural information (fracture size, orientation, etc.) plays a key role in governing the dominant physics for these systems but can only be known statistically. Traditional modeling approaches either ignore or idealize structural information at these larger scales because we lack a computational framework that utilizes it in its entirety. The work presented here integrates computational physics, machine learning and graph theory to make a paradigm shift from computationally intensive high-fidelity models to coarse-scale graphs without loss of critical structural information. We exploit the underlying discrete structure of fracture networks in systems considering flow through fractures and fracture propagation. We demonstrate that compact graph representations require significantly fewer degrees of freedom (dof) to capture micro-fracture information and further accelerate these models with Machine Learning.

58 GEOSCIENCES↗

Physics through the 1990s: Elementary-particle physics

The volume begins with a non-mathematical discussion of the motivation behind, and basic ideas of, elementary-particle physics theory and experiment. The progress over the past two decades with the quark model and unification of the electromagnetic and weak interactions is reviewed. Existing theoretical problems in the field, such as the origin of mass and the unification of the fundamental forces, are detailed, along with experimental programs to test the new theories. Accelerators, instrumentation, and detectors are described for both current and future facilities. Interactions with other areas of both theoretical and applied physics are presented. The sociology of the field is examined regarding the education of graduate students, the organization necessary in large-scale experiments, and the decision-making process involved in high-cost experiments. Finally, conclusions and recommendations for maintaining US excellence in theory and experiment are given. Appendices list both current and planned accelerators, and present statistical data on the US elementary-particle physics program. A glossary is included.

Source record↗

Effects of roughness on density-weighted particle statistics in turbulent channel flows

The distribution of inertial particles in turbulent flows is strongly influenced by the characteristics of the coherent turbulent structures which develop in the carrier flow field. In wall-bounded flows, these turbulent structures, which control the turbulent regeneration cycles, are strongly affected by the roughness of the wall, nevertheless its effects on the particle transport in two-phase turbulent flows has been still poorly investigated. The issue is discussed here by addressing DNS combined with LPT to obtain statistics of velocity and preferential accumulation of a dilute dispersion of heavy particles in a turbulent channel flow, bounded by irregular two-dimensional rough surfaces, in the one-way coupling regime.

42 ENGINEERING↗

The Sloan Digital Sky Survey Reverberation Mapping Project: An investigation of biases in C iv emission line properties

We investigate the dependence on data quality of quasar properties measured from the C iv emission line region at high redshifts. Our measurements come from 32 epochs of Sloan Digital Sky Survey Reverberation Mapping Project spectroscopic observations of 482 z>1.46 quasars. We compare the differences between measurements made from the single-epoch (SE) and coadded spectra, focusing on the C iv λ1549 emission line because of its importance for studies of high-redshift quasar demographics and physical properties, including black hole masses. In addition to statistical errors increasing (by factors of ∼2–4), we find increasing systematic offsets with decreasing signal-to-noise ratio (S/N). The systematic difference (measurement uncertainty) in our lowest-S/N (<5) subsample between the SE and coadded spectrum (i) C iv equivalent width is 17 Å (31 Å), (ii) centroid wavelength is <1 Å (2 Å), and fractional velocity widths, ΔV/V, characterized by (iii) the line dispersion, σ {sub l}, is 0.104 (0.12), and (iv) the mean absolute deviation (MAD) is 0.072 (0.11). These remain smaller than the 1σ measurement uncertainties for all subsamples considered. The MAD is found to be the most robust line-width characterization. Offsets in the C iv FWHM velocity width and the C iv profile characterized by FWHM/σ {sub l} are only smaller than the statistical uncertainties when S/N > 10, although offsets in lower-S/N spectra exceed the statistical uncertainties by only a factor of ∼1.5 and may depend on the type of functional fit to the line. Characterizing the C iv line profile by the kurtosis is the least robust property investigated, as the median systematic coadded–SE measurement differences are larger than the statistical uncertainties for all S/N subsamples.

79 ASTRONOMY AND ASTROPHYSICS↗

Likelihood-Based Climate Model Evaluation

Climate models are deterministic, mathematical descriptions of the physics of climate. Confidence in predictions of future climate is increased if the physics are verifiably correct. A necessary, (but not sufficient) condition is that past and present climate be simulated well. Quantify the likelihood that a (summary statistic computed from a) set of observations arises from a physical system with the characteristics captured by a model generated time series. Given a prior on models, we can go further: posterior distribution of model given observations.

climate models↗

Applications of the BIOPHYS Algorithm for Physically-Based Retrieval of Biophysical, Structural and Forest Disturbance Information

Canopy reflectance model inversion using look-up table approaches provides powerful and flexible options for deriving improved forest biophysical structural information (BSI) compared with traditional statistical empirical methods. The BIOPHYS algorithm is an improved, physically-based inversion approach for deriving BSI for independent use and validation and for monitoring, inventory and quantifying forest disturbance as well as input to ecosystem, climate and carbon models. Based on the multiple-forward mode (MFM) inversion approach, BIOPHYS results were summarized from different studies (Minnesota/NASA COVER; Virginia/LEDAPS; Saskatchewan/BOREAS), sensors (airborne MMR; Landsat; MODIS) and models (GeoSail; GOMS). Applications output included forest density, height, crown dimension, branch and green leaf area, canopy cover, disturbance estimates based on multi-temporal chronosequences, and structural change following recovery from forest fires over the last century. Good correspondences with validation field data were obtained. Integrated analyses of multiple solar and view angle imagery further improved retrievals compared with single pass data. Quantifying ecosystem dynamics such as the area and percent of forest disturbance, early regrowth and succession provide essential inputs to process-driven models of carbon flux. BIOPHYS is well suited for large-area, multi-temporal applications involving multiple image sets and mosaics for assessing vegetation disturbance and quantifying biophysical structural dynamics and change. It is also suitable for integration with forest inventory, monitoring, updating, and other programs.

Peddle, Derek R.↗

The joint space-time statistics of macroweather precipitation, space-time statistical factorization and macroweather models

Over the range of time scales from about 10 days to 30–100 years, in addition to the familiar weather and climate regimes, there is an intermediate “macroweather” regime characterized by negative temporal fluctuation exponents: implying that fluctuations tend to cancel each other out so that averages tend to converge. We show theoretically and numerically that macroweather precipitation can be modeled by a stochastic weather-climate model (the Climate Extended Fractionally Integrated Flux, model, CEFIF) first proposed for macroweather temperatures and we show numerically that a four parameter space-time CEFIF model can approximately reproduce eight or so empirical space-time exponents. In spite of this success, CEFIF is theoretically and numerically difficult to manage. We therefore propose a simplified stochastic model in which the temporal behavior is modeled as a fractional Gaussian noise but the spatial behaviour as a multifractal (climate) cascade: a spatial extension of the recently introduced ScaLIng Macroweather Model, SLIMM. Both the CEFIF and this spatial SLIMM model have a property often implicitly assumed by climatologists that climate statistics can be “homogenized” by normalizing them with the standard deviation of the anomalies. Physically, it means that the spatial macroweather variability corresponds to different climate zones that multiplicatively modulate the local, temporal statistics. This simplified macroweather model provides a framework for macroweather forecasting that exploits the system's long range memory and spatial correlations; for it, the forecasting problem has been solved. We test this factorization property and the model with the help of three centennial, global scale precipitation products that we analyze jointly in space and in time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Improving the Fidelity of Lattice QCD Calculations

I begin with an overview of the various statistical and systematic uncertainties that enter into lattice calculations in nuclear physics, and the hierarchy of computations needed to investigate them. I emphasize in particular the need to obtain as precise a statistical error as possible on a given ensemble of calculations, and the various variance reduction and algorithmic methods that have been proposed recently with the aim of minimizing the statistical uncertainties. I conclude with a description of the fitting methods used in lattice QCD calculations.

Richards, David↗

Engineering Super–Poissonian Photon Statistics of Spatial Light Modes

The nature of light sources is defined by the statistical fluctuations of the electromagnetic field. As such, the photon statistics of light sources are typically associated with distinct emitters. Here, the possibility of producing light beams with various photon statistics through the spatial modulation of coherent light is demonstrated. This is achieved by the sequential encoding of controllable Kolmogorov phase screens in a digital micromirror device. Interestingly, the flexibility of this scheme allows for the shaping of spatial light modes with engineered photon statistics at different spatial positions. The performance of this scheme is assessed through the photon-number-resolving characterization of different families of spatial light modes with engineered photon statistics. Furthermore, it is believed that the possibility of controlling the photon fluctuations of the light field at arbitrary spatial locations has important implications for quantum spectroscopy, sensing, and imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗