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DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS1999

Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes

Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.

Williams Colin P.↗

DOE OSTI2020

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗

DOE OSTI2018

Two-Dimensional Triplet Magnetoexcitons and a Magnetofermionic Condensate in the GaAs/AlGaAs Heterostructures

A fundamentally new collective state, namely, the magnetofermionic condensate, is discovered during photoexcitation of a sufficiently dense gas of long-lived triplet cyclotron magnetoexcitons in a twodimensional Hall insulator with a high electron mobility, a filling factor of ν = 2, and temperatures of T < 1 K. The condensed phase coherently interacts with an external electromagnetic field, exhibits superradiant properties in the recombination of correlated condensate electrons with heavy holes in the valence band, and spreads nondissipatively in the layer of a two-dimensional electron gas to macroscopical large distances, transferring an integer spin. The observed effects are explained in terms of a coherent condensate in a nonequilibrium system of two-dimensional fermions with a fully quantized energy spectrum, in which a degenerate ensemble of long-lived triplet magnetoexcitons obeying the Bose statistics is present.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

A regression-based approach to estimating retrofit savings using the Building Performance Database

Retrofitting building systems is known to provide cost-effective energy savings. This article addresses how the Building Performance Database is used to help identify potential savings. Currently, prioritizing retrofits and computing their expected energy savings and cost/benefits can be a complicated, costly, and an uncertain effort. Prioritizing retrofits for a portfolio of buildings can be even more difficult if the owner must determine different investment strategies for each of the buildings. Meanwhile, we are seeing greater availability of data on building energy use, characteristics, and equipment. These data provide opportunities for the development of algorithms that link building characteristics and retrofits empirically. In this paper we explore the potential of using such data for predicting the expected energy savings from equipment retrofits for a large number of buildings. We show that building data with statistical algorithms can provide savings estimates when detailed energy audits and physics-based simulations are not cost- or time-feasible. We develop a multivariate linear regression model with numerical predictors (e.g., operating hours, occupant density) and categorical indicator variables (e.g., climate zone, heating system type) to predict energy use intensity. The model quantifies the contribution of building characteristics and systems to energy use, and we use it to infer the expected savings when modifying particular equipment. We verify the model using residual analysis and cross-validation. We demonstrate the retrofit analysis by providing a probabilistic estimate of energy savings for several hypothetical building retrofits. We discuss the ways understanding the risk associated with retrofit investments can inform decision making. The contributions of this work are the development of a statistical model for estimating energy savings, its application to a large empirical building dataset, and a discussion of its use in informing building retrofit decisions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Status and Prospects of SBND

The Short-Baseline Near Detector (SBND) is one of the neutrino detectors positioned along the the Booster Neutrino Beam (BNB) at Fermilab. It is the near detector in the Short-Baseline Neutrino (SBN) Program, as well as having a wide-ranging independent physics program. The detector began taking neutrino data in the summer of 2024 and has already collected over 1 million neutrino events. Thanks to its unique combination of measurement resolution and statistics, SBND will soon carry out a rich program of neutrino interaction measurements and novel searches for physics beyond the Standard Model (BSM). As the SBN near detector, it will enable the full potential of the SBN sterile neutrino program by performing a precise characterisation of the unoscillated event rate and constraining BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the physics reach, current status, and future prospects of SBND are discussed.

Paton, Josephine [Fermilab]↗

DOE OSTI2014

An efficient optimization of well placement and control for a geothermal prospect under geological uncertainty

This work applies an efficient optimization technique based on a multivariate adaptive regression spline (MARS) technique to determine the optimal design and engineering of a potential geothermal production operation at a prospect near Superstition Mountain in Southern California, USA. The faster MARS-based statistical model is used as a surrogate for higher-fidelity physical models within the intensive optimization process. Its use allows for the exploration of the impacts of specific engineering design parameters in the context of geologic uncertainty as a means to both understand and maximize profitability of the production operation. The MARS model is initially developed from a training dataset generated by a finite set of computationally complex hydrothermal models applied to the prospect. Its application reveals that the optimal engineering design variables can differ considerably assuming different choices of hydrothermal flow properties, which, in turn, indicates the importance of reducing the uncertainty of key geologic properties. The major uncertainty sources in the natural-system are identified and ranked first by an efficient MARS-enabled total order sensitivity quantification, which is then used to assist evaluating the effect of geological uncertainties on optimized results. At the Southern California prospect, this parameter sensitivity analysis suggests that groundwater circulation through high permeable structures, rather than heat conduction through impermeable granite, is the primary heat transfer method during geothermal extraction. Reservoir histories simulated using optimal parameters with different constraints are analyzed and compared to investigate the longevity and maximum profit of the geothermal resources. The comparison shows that the longevity and profit are very likely to be overestimated by optimizations without appropriate constraints on natural conditions. In addition to geothermal energy production, this optimization approach can also be used to manage other geologic resource operations, such as hydrocarbon production or CO 2 sequestration, under uncertain reservoir conditions.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Inadequacy of current approaches for characterizing membrane transport properties at high salinities

Cost optimal design of osmotic membrane processes requires an accurate estimate of membrane transport parameters across their full operational range. However, standard approaches for estimating these parameters rely on empirical methods, the accuracy of which remains unquantified as a function of temperature, salinity, and measurement error. Herein, we present a systematic accuracy analysis of previously developed methods for estimation of membrane transport properties in reverse osmosis, high-pressure reverse osmosis, forward osmosis, pressure retarded reverse osmosis, and osmotically assisted reverse osmosis. We use a Monte Carlo approach to sample the full range of feasible membrane water permeabilities, salt permeabilities, structural parameters, and operating conditions for these processes. These material and process parameters are then incorporated into a physical transport model for each process. Our analysis shows that the statistical uncertainty of current empirical methods for estimating membrane parameters increases by 5 times from low-salinity to high-salinity conditions. Here, the result of this work demonstrates that empirical methods are inadequate for precisely estimating membrane transport properties at high salinity and highlight a critical need for the development of statistically validated higher accuracy methods.

42 ENGINEERING↗

DOE OSTI2014

XMM-Newton and Chandra observations of the ejecta-dominated mixed-morphology galactic supernova remnant G352.7–0.1

We present a spatial and spectral X-ray analysis of the Galactic supernova remnant (SNR) G352.7–0.1 using archival data from observations made with the XMM-Newton X-ray Observatory and the Chandra X-ray Observatory. Prior X-ray observations of this SNR had revealed a thermal center-filled morphology that contrasts with a shell-like radio morphology, thus establishing G352.7–0.1 as a member of the class of Galactic SNRs known as mixed-morphology SNRs (MMSNRs). Our study confirms that the X-ray emission comes from the SNR interior and must be ejecta dominated. Spectra obtained with XMM-Newton may be fit satisfactorily with a single thermal component (namely a non-equilibrium ionization component with enhanced abundances of silicon and sulfur). In contrast, spectra extracted by Chandra from certain regions of the SNR cannot always be fit by a single thermal component. For those regions, a second thermal component with solar abundances or two thermal components with different temperatures and thawed silicon and sulfur abundances (respectively) can generate a statistically acceptable fit. We argue that the former scenario is more physically plausible: on the basis of parameters of our spectral fits, we calculate physical parameters including X-ray emitting mass (∼45 M {sub ☉} for solar abundances). We find no evidence for overionization in the X-ray emitting plasma associated with the SNR: this phenomenon has been seen in other MMSNRs. We have conducted a search for a neutron star within the SNR by using a hard (2-10 keV) Chandra image but could not identify a firm candidate. We also present (for the first time) the detection of infrared emission from this SNR as detected at 24 μm by the MIPS on board Spitzer. Finally, we discuss the properties of G352.7–0.1 in the context of other ejecta-dominated MMSNRs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

The luminosity function at z ∼ 8 from 97 Y-band dropouts: Inferences about reionization

We present the largest search to date for Y-band dropout galaxies (z ∼ 8 Lyman break galaxies, LBGs) based on 350 arcmin{sup 2} of Hubble Space Telescope observations in the V, Y, J, and H bands from the Brightest of Reionizing Galaxies (BoRG) survey. In addition to previously published data, the BoRG13 data set presented here includes approximately 50 arcmin{sup 2} of new data and deeper observations of two previous BoRG pointings, from which we present 9 new z ∼ 8 LBG candidates, bringing the total number of BoRG Y-band dropouts to 38 with 25.5 ≤ m{sub J} ≤ 27.6 (AB system). We introduce a new Bayesian formalism for estimating the galaxy luminosity function, which does not require binning (and thus smearing) of the data and includes a likelihood based on the formally correct binomial distribution as opposed to the often-used approximate Poisson distribution. We demonstrate the utility of the new method on a sample of 97 Y-band dropouts that combines the bright BoRG galaxies with the fainter sources published in Bouwens et al. from the Hubble Ultra Deep Field and Early Release Science programs. We show that the z ∼ 8 luminosity function is well described by a Schechter function over its full dynamic range with a characteristic magnitude M{sup ⋆}=−20.15{sub −0.38}{sup +0.29}, a faint-end slope of α=−1.87{sub −0.26}{sup +0.26}, and a number density of log{sub 10} ϕ{sup ⋆}[Mpc{sup −3}]=−3.24{sub −0.24}{sup +0.25}. Integrated down to M = –17.7, this luminosity function yields a luminosity density log{sub 10} ϵ[erg s{sup −1} Hz{sup −1} Mpc{sup −3}]=25.52{sub −0.05}{sup +0.05}. Our luminosity function analysis is consistent with previously published determinations within 1σ. The error analysis suggests that uncertainties on the faint-end slope are still too large to draw a firm conclusion about its evolution with redshift. We use our statistical framework to discuss the implication of our study for the physics of reionization. By assuming theoretically motivated priors on the clumping factor and the photon escape fraction we show that the UV luminosity density from galaxy samples down to M = –17.7 can ionize only 10%-50% of the neutral hydrogen at z ∼ 8. Full reionization would require extending the luminosity function down to M = –15. The data are consistent with a substantial fraction of neutral hydrogen at z > 7, in agreement with recent suggestions based on deep spectroscopy of z ∼ 8 LBGs.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

A KINETIC TRANSPORT THEORY FOR PARTICLE ACCELERATION AND TRANSPORT IN REGIONS OF MULTIPLE CONTRACTING AND RECONNECTING INERTIAL-SCALE FLUX ROPES

Simulations of particle acceleration in turbulent plasma regions with multiple contracting and merging (reconnecting) magnetic islands emphasize the key role of temporary particle trapping in island structures for the efficient acceleration of particles to form hard power-law spectra. Statistical kinetic transport theories have been developed that capture the essential physics of particle acceleration in multi-island regions. The transport theory of Zank et al. is further developed by considering the acceleration effects of both the mean and the variance of the electric fields induced by the dynamics of multiple inertial-scale flux ropes. A focused transport equation is derived that includes new Fokker-Planck terms for particle scattering and stochastic acceleration due to the variance in multiple flux-rope magnetic fields, plasma flows, and reconnection electric fields. A Parker transport equation is also derived in which a new expression for momentum diffusion appears, combining stochastic acceleration by particle scattering in the mean multi-flux-rope electric fields with acceleration by the variance in these electric fields. Test particle acceleration is modeled analytically considering drift acceleration by the variance in the induced electric fields of flux ropes in the slow supersonic, radially expanding solar wind. Hard power-law spectra occur for sufficiently strong inertial-scale flux ropes with an index modified by adiabatic cooling, solar wind advection, and diffusive escape from flux ropes. Flux ropes might be sufficiently strong behind interplanetary shocks where the index of suprathermal ion power-law spectra observed in the supersonic solar wind can be reproduced.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Black hole evaporation in conformal gravity

We study the formation and the evaporation of a spherically symmetric black hole in conformal gravity. From the collapse of a spherically symmetric thin shell of radiation, we find a singularity-free non-rotating black hole. This black hole has the same Hawking temperature as a Schwarzschild black hole with the same mass, and it completely evaporates either in a finite or in an infinite time, depending on the ensemble. We consider the analysis both in the canonical and in the micro-canonical statistical ensembles. Last, we discuss the corresponding Penrose diagram of this physical process.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Efficient neutrino oscillation parameter inference using Gaussian processes

The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach relies on the Neyman construction of the classical confidence interval and is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this article, we propose an efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. Further, we show that in the neutrino oscillation context, one can obtain confidence intervals fives times faster in one dimension and ten times faster in two dimensions, while maintaining an accuracy above 99.5%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Measurement of branching fractions for Λ c + → n K S 0 π + and Λ c + → n K S 0 K +

Based on 4.5 fb − 1 of e + e − collision data accumulated at center-of-mass energies between 4599.53 and 4698.82 MeV with the BESIII detector, we measure the absolute branching fraction of the Cabibbo-favored decay Λ c + → n K S 0 π + with the precision improved by a factor of 2.8 and report the first evidence for the singly-Cabibbo-suppressed decay Λ c + → n K S 0 K + . The branching fractions for Λ c + → n K S 0 π + and Λ c + → n K S 0 K + are determined to be ( 1.86 ± 0.08 ± 0.04 ) × 10 − 2 and ( 3. 9 − 1.4 + 1.7 ± 0.3 ) × 1 0 − 4 , respectively, where the first uncertainties are statistical and the second ones are systematic. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2024

Observation of χ c J → p p ¯ K S 0 K − π + + c . c .

By analyzing ( 27.12 ± 0.14 ) × 10 8 ψ ( 3686 ) events collected with the BESIII detector operating at the BEPCII collider, the decays χ c J → p p ¯ K S 0 K − π + + c . c . ( J = 0 , 1 , 2 ) are observed for the first time with statistical significances greater than 10 σ . The branching fractions of these decays are determined to be B ( χ c 0 → p p ¯ K S 0 K − π + + c . c . ) = ( 2.61 ± 0.27 ± 0.32 ) × 10 − 5 , B ( χ c 1 → p p ¯ K S 0 K − π + + c . c . ) = ( 4.16 ± 0.24 ± 0.46 ) × 10 − 5 , and B ( χ c 2 → p p ¯ K S 0 K − π + + c . c . ) = ( 5.63 ± 0.28 ± 0.46 ) × 10 − 5 , respectively. The processes χ c 1 , 2 → p ¯ Λ ( 1520 ) K S 0 π + + c . c . are also observed, with statistical significances of 5.7 σ and 7.0 σ , respectively. Evidence for χ c 0 → p ¯ Λ ( 1520 ) K S 0 π + + c . c . is found with statistical significances of 3.3 σ . The corresponding branching fractions are determined to be B ( χ c 0 → p ¯ Λ ( 1520 ) K S 0 π + + c . c . ) = ( 1.6 1 − 0.64 + 0.68 ± 0.23 ) × 10 − 5 , B ( χ c 1 → p ¯ Λ ( 1520 ) K S 0 π + + c . c . ) = ( 4.06 − 0.76 + 0.80 ± 0.52 ) × 10 − 5 , and B ( χ c 2 → p ¯ Λ ( 1520 ) K S 0 π + + c . c . ) = ( 4.0 9 − 0.84 + 0.87 ± 0.42 ) × 10 − 5 . Here, the first uncertainties are statistical and the second ones are systematic. Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2024

Observation of χ c 0 → Σ + Σ ¯ − η and evidence for χ c 1 , 2 → Σ + Σ ¯ − η

Using ( 27.12 ± 0.14 ) × 10 8 ψ ( 3686 ) events collected with the BESIII detector, the decay χ c 0 → Σ + Σ ¯ − η is observed for the first time with a signal significance of 7.0 σ , and evidence for χ c 1 → Σ + Σ ¯ − η and χ c 2 → Σ + Σ ¯ − η is found with signal significances of 4.3 σ and 4.6 σ , respectively. The branching fractions are determined to be B ( χ c 0 → Σ + Σ ¯ − η ) = ( 1.26 ± 0.20 ± 0.13 ) × 10 − 4 , B ( χ c 1 → Σ + Σ ¯ − η ) = ( 5.10 ± 1.21 ± 0.67 ) × 10 − 5 , and B ( χ c 2 → Σ + Σ ¯ − η ) = ( 5.46 ± 1.18 ± 0.50 ) × 10 − 5 , where the first uncertainties are statistical, and the second ones are systematic. Published by the American Physical Society 2024

Ablikim, M.↗

DOE OSTI2020

Big Data Health Physics

This project, in completion of the Illinois Institute of Technology’s Professional Masters of Health Physics program, represents a two part independent study wherein (1) a Statistical Analysis is performed on a new data process implemented at a Los Alamos National Laboratory facility and (2) a Project Management Plan is designed for the successful implementation of this new radiation protection management process.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

ResStock Measure Documentation: Residential Two-Stage Geothermal Heat Pump (4.0 COP, 20.5 EER) With Envelope Improvements and Advanced Air Sealing

The goal of this work is to develop energy efficiency, demand flexibility, and other retrofit end-use load shapes (electricity, gas, propane, or fuel oil) that cover a majority of the high-impact, market-ready (or nearly market-ready) measures. "Measures" refers to retrofits that can be applied to buildings during modeling. An "end-use savings shape" is the difference in energy consumption between a baseline building and a building with an energy efficiency, demand flexibility, or other retrofit measure applied. It results in a time-series profile that is broken down by end use and fuel (electricity or on-site gas, propane, or fuel oil use) at each time step. ResStock is a highly granular, physics-based, bottom-up model that uses multiple data sources, statistical sampling methods, and advanced building energy simulations to estimate the annual subhourly energy consumption of the residential building stock across the United States. The baseline model intends to represent the U.S. residential building stock as it existed in 2018. Technical documentation for the inputs and assumptions in the baseline building stock model is available in Reyna et al. (2025). Calibration and validation of the baseline model results are available in the final technical report of the End-Use Load Profiles project (Wilson, et al. 2022). This document focuses on a single end-use savings shape measure: Residential Two-Stage Geothermal Heat Pump (GHP) (4.0 COP, 20.5 EER) With Envelope Improvements. This measure combines a two-stage GHP with envelope improvements as a single package. As this package is a combination of two other measures, this document focused on documenting the results associated with this combination of technologies, with individual measure documents for two-stage GHPs and envelope improvements providing the information on the details of these measures. When the two technologies are combined, envelope improvements can modestly reduce energy consumption by a further 10%-15%, but also reduce the required size of the ground heat exchanger and heat pump by approximately 33% on average across all sites. The cost of installing envelope improvements in these homes is likely to be more than paid for by the reduction in equipment and drilling costs in these buildings for the majority of the stock.

15 GEOTHERMAL ENERGY↗