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Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
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Chemomechanics in alloy phase stability
We describe a first-principles statistical mechanics method to calculate the free energies of crystalline alloys that depend on temperature, composition, and strain. The approach relies on an extension of the alloy cluster expansion to include an explicit dependence on homogeneous strain in addition to site occupation variables that track the degree of chemical ordering. The method is applied to the Si-Ge binary alloy and is used to calculate free energies that describe phase stability under arbitrary epitaxial constraints. We find that while the incoherent phase diagram (in which coexisting phases are not affected by coherency constraints) hosts a miscibility gap, coherent phase equilibrium predicts ordering and negative enthalpies of mixing. Instead of chemical instability, the chemomechanical free energy exhibits instabilities along directions that couple the composition of the alloy with a volumetric strain order parameter. Furthermore, this has fundamental implications for phase field models of spinodal decomposition as it indicates the importance of gradient energy coefficients that couple gradients in composition with gradients in strain.
Extracting Topological Orders of Generalized Pauli Stabilizer Codes in Two Dimensions
In this paper, we introduce an algorithm for extracting topological data from translation invariant generalized Pauli stabilizer codes in two-dimensional systems, focusing on the analysis of anyon excitations and string operators. The algorithm applies to Z d qudits, including instances where d is a nonprime number. This capability allows the identification of topological orders that differ from the Z d toric codes. It extends our understanding beyond the established theorem that Pauli stabilizer codes for Z p qudits (with p being a prime) are equivalent to finite copies of Z p toric codes and trivial stabilizers. The algorithm is designed to determine all anyons and their string operators, enabling the computation of their fusion rules, topological spins, and braiding statistics. The method converts the identification of topological orders into computational tasks, including Gaussian elimination, the Hermite normal form, and the Smith normal form of truncated Laurent polynomials. Furthermore, the algorithm provides a systematic approach for studying quantum error-correcting codes. We apply it to various codes, such as self-dual CSS quantum codes modified from the two-dimensional honeycomb color code and non-CSS quantum codes that contain the double semion topological order or the six-semion topological order. Published by the American Physical Society 2024
Algorithm for systematic peak extraction from atomic pair distribution functions
Abstract not provided
Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System With Machine Learning and Statistical Models
We benchmark performance of long-short term memory (LSTM) network machine learning model and autoregressive integrated moving average (ARIMA) statistical model in temporal forecasting of distributed temperature sensing (DTS). Data in this study consists of fluid temperature transient measured with two co-located Rayleigh scattering fiber optic sensors (FOS) in a forced convection mixing zone of a thermal tee. We treat each gauge of a FOS as an independent temperature sensor. We first study prediction of DTS time series using Vanilla LSTM and ARIMA models trained on prior history of the same FOS that is used for testing. The results yield maximum absolute percentage error (MaxAPE) and root mean squared percentage error (RMSPE) of 1.58% and 0.06% for ARIMA, and 3.14% and 0.44% for LSTM, respectively. Next, we investigate zero-shot forecasting (ZSF) with LSTM and ARIMA trained on history of the co-located FOS only, which is advantageous when limited training data is available. The ZSF MaxAPE and RMSPE values for ARIMA are comparable to those of the Vanilla use case, while the error values for LSTM increase. We show that in ZSF, performance of LSTM network can be improved by training on most correlated gauges between the two FOS, which are identified by calculating the Pearson correlation coefficient. The improved ZSF MaxAPE and RMSPE for LSTM are 4.4% and 0.33%, respectively. Performance of ZSF LSTM can be further enhanced through transfer learning (TL), where LSTM is re-trained on a subset of the FOS that is the target of forecasting. We show that LSTM pre-trained on correlated dataset and re-trained on 30% of testing target dataset achieves MaxAPE and RMSPE values of 2.32% and 0.28%, respectively.
A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions
Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.
Data-driven Feature Analysis in Control Design for Series-Compensated Transmission Systems
One challenge in power-system control designs is the gap between numerical model-based analysis and complex real-world power systems. With increased data and measurements being collected from power systems, data-driven analysis (e.g., machine learning) may provide an alternative approach to reveal hidden information through learning from the real system data, and provide insights for better control scheme design during the utility planning process. Data-driven feature analysis was performed to evaluate the relationships between series compensation, power generation, and path flows in a real transmission system, as well as temporal patterns. The main data-driven analysis methods, including statistical cross-correlation, multinomial logistical regression, and classification and regression trees, were integrated for feature selection and developing predictive models of series compensation. Analysis results demonstrated the effectiveness of the proposed methodology in feature analysis and the potential to help improve power-system control scheme design.
Data-Driven Load Modeling and Forecasting of Residential Appliances
The expansion of residential demand side management programs and increased deployment of controllable loads require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. We also propose an algorithm for short-term load forecasting as a key application for appliance-level load models. Case studies performed using granular sub-metered power measurements from various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.
Impact of thermodynamic fluctuations and pattern size on the nucleation behavior during area selective deposition
In this work, the authors explore the impact that thermodynamic fluctuations have on the spontaneous appearance of defects during area selective deposition on patterned surfaces that are fully covered by adsorbates under local thermodynamic equilibrium, such as self-assembled monolayers. By using a simple lattice gas model for the adsorbed monolayer, the authors were able to track the spontaneous formation of defects in the monolayer as a function of the pattern width. The results indicate that, for pattern widths of the order of tens of nanometers, roughening effects at the pattern edge can be the leading source for the spontaneous appearance of nucleation defects. This leads to an enhancement of the density of defects that can be up to three orders of magnitude higher than those expected in uniform (not patterned) surfaces. The model also predicts a density of defects that is inversely proportional to the pattern width. Finally, if the dynamic nucleation of defects during area selective deposition is driven by thermodynamic fluctuations, the model predicts that the nucleation rate should be proportional to the total precursor fluency and independent of purge times. Moreover, a tight confinement of the monolayer through a high quality smooth interface in the patterned substrate and strong cohesive interactions between adsorbates should each contribute to a reduction of the overall defect density.
SU-E-J-263: Repeatability of SUV and Texture Parameters in Serial PET Studies
Purpose: Standardized uptake values (SUV) are standard quantitative PET measures of FDG tumor uptake used,and are used as a tool to monitor response to therapy. Textural analysis is emerging as a new tool for assessing intratumoral heterogeneity which may allow better tissue characterization and improved prediction of response and survival rate.Understanding what variations may be expected in these parameters is key in order to make decisions based on how the change throughout the course of treatment. The aim of this study was to assess repeatability in SUV measures and texture parameters,and establish criteria that differentiate changes associated with treatment rather than statistical variability. Methods: Eighty patients,167 random lesions total,were scanned in a GE Discovery STE PET/CT Scanner. One field-of-view was chosen centered on the largest lesion observed in a clinical whole-body FDG PET.Immediately following,a gated 9 min scan was acquired in list mode,without changing the patient’s position between any scans. Data was replayed into 3 time bins,3 min each,in order to insure equivalent noise characteristics in each replicate.Data was reconstructed into 128×128×47 square matrices.One VOI was drawn over each lesion for each patient and used to segment all 3 replicates. The mean.max and peak SUV were calculated for each VOI and replicate. First-order textural features were also calculated (skewness and kurtosis). Repeatability was calculated as the average standard deviation over the mean for the 3 repeated measurements for each lesion. Results: The average percent error in the SUV max,peak and mean were 3.4%(0– 12.9%),1.9% (0–7.5%),2.8% (0–12.2%),respectively.For skewness and kurtosis they were 10.9% and 17.8%. Conclusion: We have shown that there is a large variation in %error in SUV measures across patients. SUVpeak is the least variable and kurtosis and skewness parameters are less reliable thatn SUVs.Higher order textures are be.
Genome-Scale Transcription-Translation Mapping Reveals Features of Zymomonas mobilis Transcription Units and Promoters
Efforts to rationally engineer synthetic pathways in Zymomonas mobilis are impeded by a lack of knowledge and tools for predictable and quantitative programming of gene regulation at the transcriptional, posttranscriptional, and posttranslational levels. With the detailed functional characterization of the Z. mobilis genome presented in this work, we provide crucial knowledge for the development of synthetic genetic parts tailored to Z. mobilis . This information is vital as researchers continue to develop Z. mobilis for synthetic biology applications. Our methods and statistical analyses also provide ways to rapidly advance the understanding of poorly characterized bacteria via empirical data that enable the experimental validation of sequence-based prediction for genome characterization and annotation.
Dynamic generation of spin-wave currents in hybrid structures
Spin transport through the interface in a semiconductor/ferromagnetic insulator hybrid structure is studied by the nonequilibrium statistical operator method under conditions of the spin Seebeck effect. The effective parameter approach in which each examined subsystem (conduction electrons, magnons, phonons) is characterized by its specific effective temperature is considered. The effect of the resonant (electric dipole) excitation of the spin electronic subsystem of conduction electrons on spin-wave current excitation in a ferromagnetic insulator is considered. The macroscopic equations describing the spin-wave current caused by both resonant excitation of the spin system of conduction electrons and the presence of a nonuniform temperature field in the ferromagnetic insulator are derived taking into account both the resonance-diffusion propagation of magnons and their relaxation processes. It is shown that spin-wave current excitation is also of resonant nature under the given conditions.
QLBT: a linear Boltzmann transport model for heavy quarks in a quark-gluon plasma of quasi-particles
Abstract We develop a new heavy quark transport model, QLBT, to simulate the dynamical propagation of heavy quarks inside the quark-gluon plasma (QGP) created in relativistic heavy-ion collisions. Our QLBT model is based on the linear Boltzmann transport (LBT) model with the ideal QGP replaced by a collection of quasi-particles to account for the non-perturbative interactions among quarks and gluons of the hot QGP. The thermal masses of quasi-particles are fitted to the equation of state from lattice QCD simulations using the Bayesian statistical analysis method. Combining QLBT with our advanced hybrid fragmentation-coalescence hadronization approach, we calculate the nuclear modification factor $$R_\mathrm {AA}$$ R AA and the elliptic flow $$v_2$$ v 2 of D mesons at the Relativistic Heavy-Ion Collider and the Large Hadron Collider. By comparing our QLBT calculation to the experimental data on the D meson $$R_\mathrm {AA}$$ R AA and $$v_2$$ v 2 , we extract the heavy quark transport parameter $$\hat{q}$$ q ^ and diffusion coefficient $$D_\mathrm {s}$$ D s in the temperature range of $$1-4~T_\mathrm {c}$$ 1 - 4 T c , and compare them with the lattice QCD results and other phenomenological studies.
Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration
Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.
XRD-PUAT (X-Ray Diffraction - Parameter Uncertainty Analysis Toolkit)
This XRD uncertainty toolkit was built to investigate uncertainty and local least squares topology over a specified parameter space on a single histogram in a gpx GSAS-II file. The parameter uncertainty can be investigated using either a frequentist F-test approach or a Bayesian Inference statistical inversion method on a weighted least squares or peak fit refinements.SAND Number: SAND2020-12227 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
ForceFinder
SAND2025-11750O ForceFinder extends the Structural Dynamics Python Libraries (SDynPy) with comprehensive tools for inverse source estimation (ISE) tasks via frequency response function (FRF) matrix inversion. The software is designed for transfer path analysis and multiple-input/multiple-output (MIMO) vibration control problems. It allows users to estimate sources through various algorithms, from the basic Moore-Penrose pseudo-inverse to statistical learning methods such as Tikhonov regularization via an L-curve and elastic net regularization via an information criterion. ForceFinder uses an object-oriented framework, where all components of the ISE problem—such as FRFs, responses, and transformations—are stored in a "SourcePathReceiver" object. This software can be applied to any noise and vibration problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Analysis of Cloud-Resolving Model Simulations for Scale Dependence of Convective Momentum Transport
Abstract We use 3D cloud-resolving model (CRM) simulations of two mesoscale convective systems at midlatitudes and a simple statistical ensemble method to diagnose the scale dependency of convective momentum transport (CMT) and CMT-related properties and evaluate a parameterization scheme for the convection-induced pressure gradient (CIPG) developed by Gregory et al. Gregory et al. relate CIPG to a constant coefficient multiplied by mass flux and vertical mean wind shear. CRM results show that mass fluxes and CMT exhibit strong scale dependency in temporal evolution and vertical structure. The upgradient–downgradient CMT characteristics for updrafts are generally similar between small and large grid spacings, which is consistent with previous understanding, but they can be different for downdrafts across wide-ranging grid spacings. For the small to medium grid spacings (4–64 km), Gregory et al. reproduce some aspects of CIPG scale dependency except for underestimating the variations of CIPG as grid spacing decreases. However, for large grid spacings (128–512 km), Gregory et al. might even less adequately parameterize CIPG because it omits the contribution from either the nonlinear-shear or the buoyancy forcings. Further diagnosis of CRM results suggests that inclusion of nonlinear-shear forcing in Gregory et al. is needed for the large grid spacings. For the small to median grid spacings, a modified Gregory et al. with the three-updraft approach help better capture the variations of CIPG as grid spacing decreases compared to the single updraft approach. Further, the optimal coefficients used in Gregory et al. seem insensitive to grid spacings, but they might be different for updrafts and downdrafts, for different MCS types, and for zonal and meridional components.