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DOE OSTI2025

Neutrino Induced Charged Current Coherent Pion Production for Constraining the Muon Neutrino Flux at DUNE

We study neutrino induced charge current coherent pion production (νμ CC-Coh π) as a tool for constraining the neutrino flux at the Deep Underground Neutrino Experiment (DUNE). The neutrino energy and flavor in the process can be directly reconstructed from the outgoing particles, making it especially useful to specifically constrain the muon neutrino component of the total flux. The cross section of this process can be obtained using the Adler relation with the π-Ar elastic scattering cross section, taken either from external data or, as we explore, from a simultaneous measurement in the DUNE near detector. We develop a procedure that leverages νμ CC-Coh π events to fit for the neutrino flux while simultaneously accounting for relevant effects in the cross section. We project that this method has the statistical power to constrain the uncertainty on the normalization of the flux at its peak to a few percent. This study demonstrates the potential utility of aνμ CC-Coh π flux constraint, though further work will be needed to determine the range of validity and precision of the Adler relation upon which it relies, as well as to measure the π-Ar elastic scattering cross section to the requisite precision. We discuss the experimental and phenomenological developments necessary to unlock the νμ CC-Coh π process as a "standard candle'' for neutrino experiments.

Putnam, Gray [Fermilab]↗

DOE OSTI2022

Deep Generative Models for Materials Discovery and Machine Learning-Accelerated Innovation

Machine learning and artificial intelligence (AI/ML) methods are beginning to have significant impact in chemistry and condensed matter physics. For example, deep learning methods have demonstrated new capabilities for high-throughput virtual screening, and global optimization approaches for inverse design of materials. Recently, a relatively new branch of AI/ML, deep generative models (GMs), provide additional promise as they encode material structure and/or properties into a latent space, and through exploration and manipulation of the latent space can generate new materials. These approaches learn representations of a material structure and its corresponding chemistry or physics to accelerate materials discovery, which differs from traditional AI/ML methods that use statistical and combinatorial screening of existing materials via distinct structure-property relationships. However, application of GMs to inorganic materials has been notably harder than organic molecules because inorganic structure is often more complex to encode. In this work we review recent innovations that have enabled GMs to accelerate inorganic materials discovery. We focus on different representations of material structure, their impact on inverse design strategies using variational autoencoders or generative adversarial networks, and highlight the potential of these approaches for discovering materials with targeted properties needed for technological innovation.

36 MATERIALS SCIENCE↗

DOE OSTI2024

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study

Background The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. Methods This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t -tests, Pearson correlation analyses, and mediation effect analysis. Results The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058–0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185–0.327). Conclusion The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Chai, Yulin↗

DOE OSTI2017

Low-frequency Carbon Radio Recombination Lines. I. Calculations of Departure Coefficients

In the first paper of this series, we study the level population problem of recombining carbon ions. We focus our study on high quantum numbers, anticipating observations of carbon radio recombination lines to be carried out by the Low Frequency Array. We solve the level population equation including angular momentum levels with updated collision rates up to high principal quantum numbers. We derive departure coefficients by solving the level population equation in the hydrogenic approximation and including low-temperature dielectronic capture effects. Our results in the hydrogenic approximation agree well with those of previous works. When comparing our results including dielectronic capture, we find differences that we ascribe to updates in the atomic physics (e.g., collision rates) and to the approximate solution method of the statistical equilibrium equations adopted in previous studies. A comparison with observations is discussed in an accompanying article, as radiative transfer effects need to be considered.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

A Cross-correlation Study of High-energy Neutrinos and Tracers of Large-scale Structure

The origin of the bulk of the astrophysical neutrinos detected by the IceCube Observatory remains a mystery. Previous source-finding analyses compared the directions of IceCube events and individual sources in astrophysical catalogs. The source association method is technically challenging when the number of source candidates is much larger than the number of the observed astrophysical neutrinos. In this paper, we show that in this large source number regime, a two-point cross-correlation analysis of neutrino data and source catalog can instead be used to constrain potential source populations for the high-energy astrophysical neutrinos, and provide spatial evidence for the existence of astrophysical neutrinos. We present an analysis of the cross-correlation of the IceCube 2010–2012 point-source data and a WISE–2MASS galaxy sample. While we find no significant detection of cross-correlation with the publicly available neutrino data set, we show that, when applied to the full IceCube data, which has a longer observation time and higher astrophysical neutrino purity, our method has sufficient statistical power to detect a cross-correlation signal if the neutrino sources trace the large-scale structure of the universe.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

A Dark Siren Measurement of the Hubble Constant with the LIGO/Virgo Gravitational Wave Event GW190412 and DESI Galaxies

Abstract We present a measurement of the Hubble Constant H 0 using the gravitational wave event GW190412, an asymmetric binary black hole merger detected by LIGO/Virgo, as a dark standard siren. This event does not have an electromagnetic counterpart, so we use the statistical standard siren method and marginalize over potential host galaxies from the Dark Energy Spectroscopic Instrument (DESI) survey. GW190412 is well-localized to 12 deg 2 (90% credible interval), so it is promising for a dark siren analysis. The dark siren value for H 0 = 85.4 − 33.9 + 29.1 km s −1 Mpc −1 , with a posterior shape that is consistent with redshift overdensities. When combined with the bright standard siren measurement from GW170817 we recover H 0 = 77.96 − 5.03 + 23.0 km s −1 Mpc −1 , consistent with both early and late-time Universe measurements of H 0 . This work represents the first standard siren analysis performed with DESI data, and includes the most complete spectroscopic sample used in a dark siren analysis to date.

Ballard, W.↗

DOE OSTI2015

Direct Observation of Ultralow Vertical Emittance using a Vertical Undulator

In recent work, the first quantitative measurements of electron beam vertical emittance using a vertical undulator were presented, with particular emphasis given to ultralow vertical emittances [K. P. Wootton, et al., Phys. Rev. ST Accel. Beams, 17, 112802 (2014)]. Using this apparatus, a geometric vertical emittance of 0.9 ± 0.3 pm rad has been observed. A critical analysis is given of measurement approaches that were attempted, with particular emphasis on systematic and statistical uncertainties. The method used is explained, compared to other techniques and the applicability of these results to other scenarios discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

WEC Design Response Toolbox v. 1.0

The WEC Design Response Toolbox (WDRT) is a numerical toolbox for design-response analysis of wave energy converters (WECs). The WDRT was developed during a series of efforts to better understand WEC survival design. The WDRT has been designed as a tool for researchers and developers, enabling the straightforward application of statistical and engineering methods. The toolbox includes methods for short-term extreme response, environmental characterization, long-term extreme response and risk analysis, fatigue, and design wave composition.

Coe, Ryan↗

DOE OSTI2018

Multi-scale theory-assisted nano-engineering of plasmonic-organic hybrid electro-optic device performance

Multi-scale (correlated quantum and statistical mechanics) modeling methods have been advanced and employed to guide the improvement of organic electro-optic (OEO) materials, including by analyzing electric field poling induced electro-optic activity in nanoscopic plasmonic-organic hybrid (POH) waveguide devices. The analysis of in-device electro-optic activity emphasizes the importance of considering both the details of intermolecular interactions within organic electro-optic materials and interactions at interfaces between OEO materials and device architectures. Dramatic improvement in electro-optic device performance--including voltage-length performance, bandwidth, energy efficiency, and lower optical losses have been realized. These improvements are critical to applications in telecommunications, computing, sensor technology, and metrology. Multi-scale modeling methods illustrate the complexity of improving the electro-optic activity of organic materials, including the necessity of considering the trade-off between improving poling-induced acentric order through chromophore modification and the reduction of chromophore number density associated with such modification. Computational simulations also emphasize the importance of developing chromophore modifications that serve multiple purposes including matrix hardening for enhanced thermal and photochemical stability, control of matrix dimensionality, influence on material viscoelasticity, improvement of chromophore molecular hyperpolarizability, control of material dielectric permittivity and index of refraction properties, and control of material conductance. Consideration of new device architectures is critical to the implementation of chipscale integration of electronics and photonics and achieving the high bandwidths for applications such as next generation (e.g., 5G) telecommunications.

Elder, Delwin L.↗

DOE OSTI2019

Quantification of the systematic uncertainties associated with measuring a chance coincidence background at Chi-Nu

Modern advances in waveform digitizers allow coincidence experiments to be performed by recording singles data, and identifying coincidences in a later software analysis. It has been previously shown that for such data, the coincidence background can be obtained with unprecedented statistical precision. This method is especially attractive for experiments with complicated, time-varying backgrounds (i.e. not amenable to analytic or even a detailed Monte Carlo analysis). Many neutron detection experiments fall into this category, such as the Chi-Nu experiment to measure the prompt fission neutron spectra of the major actinides. While it is believed that use of this method will yield an improved systematic precision of the measured background, this has not previously been demonstrated. Presented here is the first analysis of the systematic uncertainties associated with this method

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2016

Effective Long-term Monitoring Strategies by Integrating Reactive Transport Models with In situ Geochemical Measurements - 16212

Long-term monitoring of contaminant transport in groundwater is expected to account for a large fraction of future life-cycle cleanup costs at the DOE sites. The Attenuation-Based Remedies in the Subsurface Applied Field Research Initiative (ABRS AFRI) is developing an innovative approach for cost-effective in situ long-term monitoring. The approach is based on strategically adding the measurements of controlling variables (such as pH, redox potential, electrical conductivity, and groundwater level), which control the plume mobility and its spatial and temporal distribution. In situ measurements of these variables - supplemented with a reduced number of standard periodic groundwater samples - are expected to lead to more cost-effective monitoring, and can also serve as an early warning system for detecting unexpected plume migration. The objective of this study is to support the development of such a long-term monitoring approach through the application of advanced computational methods, including (1) statistical data mining and analysis, and (2) three-dimensional flow and reactive chemical transport modeling. We have performed the statistical data analysis of historical and current monitoring data at the Savannah River Site (SRS) F-Area to quantify the correlations between the controlling variables and radioactive contaminant concentrations. In parallel, we have used 3D modeling of flow and contaminant transport to provide the prediction of the contaminant plume evolution. The results of both data analysis and modeling confirmed that the correlations between controlling variables and contaminant concentrations are significant. Modeling results also suggest that the correlation parameters will change in the future, which is important to assess the long-term efficacy of the proposed monitoring approach. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Statistically informed upscaling of damage evolution in brittle materials

The presence and growth of micro-cracks degrade the strength of brittle solids, greatly impacting the overall material response. Hence, the evolution of these micro-cracks must be accounted for in models describing the relationship between stress and strain so that accurate predictions of material failure can be made. The evolution of individual cracks and crack networks can be simulated with high-fidelity microscale models utilizing highly resolved meshes that can be computationally expensive to a point in which it limits the simulation scale. Hence, for many engineering applications that require simulations of large components, continuum-scale models, which cannot explicitly resolve individual cracks and thus lose important physical information, are required. In this work, we bridge these two scales by developing and implementing a continuum-scale effective moduli constitutive model that is informed by crack statistics generated from a high-fidelity model resolved using a finite-discrete element method (FDEM) implementation. Using statistical information describing the evolution of crack lengths and orientations, this model can capture the effects of brittle damage evolution without the need to resolve individual cracks. We have successfully captured the stress-strain behavior of the high-fidelity simulations using the statistics-based constitutive model shown through direct comparison of stress-strain curves. The curves match within error bars present in the strain-softening portions of the stress-strain curve of the high-fidelity results due to the statistical variation of the initial pre-existing crack network. In conclusion, the stress-strain curves are also compared to experimental results for similar loading conditions and show good qualitative agreement.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Informed total-error-minimizing priors: Interpretable cosmological parameter constraints despite complex nuisance effects

While Bayesian inference techniques are standard in cosmological analyses, it is common to interpret resulting parameter constraints with a frequentist intuition. This intuition can fail, for example, when marginalizing high-dimensional parameter spaces onto subsets of parameters, because of what has come to be known as projection effects or prior volume effects. We present the method of informed total-error-minimizing (ITEM) priors to address this problem. An ITEM prior is a prior distribution on a set of nuisance parameters, such as those describing astrophysical or calibration systematics, intended to enforce the validity of a frequentist interpretation of the posterior constraints derived for a set of target parameters (e.g., cosmological parameters). Our method works as follows. For a set of plausible nuisance realizations, we generate target parameter posteriors using several different candidate priors for the nuisance parameters. We reject candidate priors that do not accomplish the minimum requirements of bias (of point estimates) and coverage (of confidence regions among a set of noisy realizations of the data) for the target parameters on one or more of the plausible nuisance realizations. Of the priors that survive this cut, we select the ITEM prior as the one that minimizes the total error of the marginalized posteriors of the target parameters. As a proof of concept, we applied our method to the density split statistics measured in Dark Energy Survey Year 1 data. We demonstrate that the ITEM priors substantially reduce prior volume effects that otherwise arise and that they allow for sharpened yet robust constraints on the parameters of interest.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

A fixed mass method for the Kramers-Moyal expansion—Application to time series with outliers

Extraction of stochastic and deterministic components from empirical data—necessary for the reconstruction of the dynamics of the system—is discussed. We determine both components using the Kramers-Moyal expansion. In our earlier papers, we obtained large fluctuations in the magnitude of both terms for rare or extreme valued events in the data. Calculations for such events are burdened by an unsatisfactory quality of the statistics. In general, the method is sensitive to the binning procedure applied for the construction of histograms. Instead of the commonly used constant width of bins, we use here a constant number of counts for each bin. This approach—the fixed mass method—allows to include in the calculation events, which do not yield satisfactory statistics in the fixed bin width method. The method developed is general. To demonstrate its properties, here, we present the modified Kramers-Moyal expansion method and discuss its properties by the application of the fixed mass method to four representative heart rate variability recordings with different numbers of ectopic beats. These beats may be rare events as well as outlying, i.e., very small or very large heart cycle lengths. The properties of ectopic beats are important not only for medical diagnostic purposes but the occurrence of ectopic beats is a general example of the kind of variability that occurs in a signal with outliers. To show that the method is general, we also present results for two examples of data from very different areas of science: daily temperatures at a large European city and recordings of traffics on a highway. Using the fixed mass method, to assess the dynamics leading to the outlying events we studied the occurrence of higher order terms of the Kramers-Moyal expansion in the recordings. We found that the higher order terms of the Kramers-Moyal expansion are negligible for heart rate variability. This finding opens the possibility of the application of the Langevin equation to the whole range of empirical signals containing rare or outlying events. Note, however, that the higher order terms are non-negligible for the other data studied here and for it the Langevin equation is not applicable as a model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Assessment of software methods for estimating protein-protein relative binding affinities

A growing number of computational tools have been developed to accurately and rapidly predict the impact of amino acid mutations on protein-protein relative binding affinities. Such tools have many applications, for example, designing new drugs and studying evolutionary mechanisms. In the search for accuracy, many of these methods employ expensive yet rigorous molecular dynamics simulations. By contrast, non-rigorous methods use less exhaustive statistical mechanics, allowing for more efficient calculations. However, it is unclear if such methods retain enough accuracy to replace rigorous methods in binding affinity calculations. This trade-off between accuracy and computational expense makes it difficult to determine the best method for a particular system or study. Here, eight non-rigorous computational methods were assessed using eight antibody-antigen and eight non-antibody-antigen complexes for their ability to accurately predict relative binding affinities (ΔΔG) for 654 single mutations. In addition to assessing accuracy, we analyzed the CPU cost and performance for each method using a variety of physico-chemical structural features. This allowed us to posit scenarios in which each method may be best utilized. Most methods performed worse when applied to antibody-antigen complexes compared to non-antibody-antigen complexes. Rosetta-based JayZ and EasyE methods classified mutations as destabilizing (ΔΔG < -0.5 kcal/mol) with high (83–98%) accuracy and a relatively low computational cost for non-antibody-antigen complexes. Some of the most accurate results for antibody-antigen systems came from combining molecular dynamics with FoldX with a correlation coefficient (r) of 0.46, but this was also the most computationally expensive method. Overall, our results suggest these methods can be used to quickly and accurately predict stabilizing versus destabilizing mutations but are less accurate at predicting actual binding affinities. This study highlights the need for continued development of reliable, accessible, and reproducible methods for predicting binding affinities in antibody-antigen proteins and provides a recipe for using current methods.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Evaluating Space Object Conjunction Probabilities Using Characteristic Function Inversion

This report discusses an approach to computing the probability of a conjunction between two space objects in the short-term encounter scenario. A conjunction is defined here as an event where the miss distance between the objects is less than some specified value. The scenario assumptions are that the motion of the objects is linear, their positions are Gaussian distributed, and their velocities are known and constant. Under these assumptions, the squared-miss distance is shown to have the generalized chi-square distribution. An established statistical technique called characteristic function inversion is employed to evaluate the distribution and obtain conjunction probabilities. The method is closely related to a recent approach based on moment generating function inversion, and a qualitative comparison of the approaches is provided. Last, the method is tested on several benchmark test cases where it agrees with numerical integration on the cases with conjunction probabilities above 10 –12 . However, the exact probability in these cases is usually not needed and this probability can be bounded above using an independent Gaussian approximation. Overall, the report shows how to compute conjunction probabilities using a standard statistical method, though numerical integration seems to perform equally well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

An algebraic method for constructing stable and consistent autoregressive filters

In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗