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At least 361 records · Page 20

Derivation of the Statistical Distribution of the Mass Peak Centroids of Mass Spectrometers Employing Analog-to-Digital Converters and Electron Multipliers

Here, the mass peak centroid is a quantity that is at the core of mass spectrometry (MS). However, despite its central status in the field, models of its statistical distribution are often chosen quite arbitrarily and without attempts at establishing a proper theoretical justification for their use. Recent work has demonstrated that for mass spectrometers employing analog-to-digital converters (ADCs) and electron multipliers, the statistical distribution of the mass peak intensity can be described via a relatively simple model derived essentially from first principles. Building on this result, the following article derives the corresponding statistical distribution for the mass peak centroids of such instruments. It is found that for increasing signal strength, the centroid distribution converges to a Gaussian distribution whose mean and variance are determined by physically meaningful parameters and which in turn determine bias and variability of the m/z measurements of the instrument. Through the introduction of the concept of “pulse-peak correlation”, the model also elucidates the complicated relationship between the shape of the voltage pulses produced by the preamplifier and the mean and variance of the centroid distribution. The predictions of the model are validated with empirical data and with Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗

Conformational Populations of β-(1→4) O -Glycosidic Linkages Using Redundant NMR J -Couplings and Circular Statistics

Twelve disaccharides containing β-(1→4) linkages and displaying systematic structural variations in the vicinity of these linkages were selectively labeled with 13 C to facilitate measurements of multiple NMR spin–spin (scalar; J) coupling constants (J CH and J CC values) across their O-glycosidic linkages. Ensembles of spin-couplings ( 2 J COC , 3 J COCH , 3 J COCC ) sensitive to the two linkage torsion angles, phi (Φ) and psi (ψ), were analyzed by using parametrized equations obtained from density functional theory (DFT) calculations, Fredholm theory, and circular statistics to calculate experiment-based rotamer populations for Φ and ψ in each disaccharide. With the statistical program MA’AT, torsion angles Φ and ψ were modeled as a single von Mises distribution, which yielded two parameters, the mean position and the circular standard deviation (CSD) for each angle. The NMR-derived rotamer populations were compared to those obtained from 1 μs aqueous molecular dynamics (MD) simulations and crystallographic database statistical analyses. Conformer populations obtained exclusively from the MA’AT treatment of redundant J-couplings were in very good agreement with those obtained from the MD simulations, providing evidence that conformational populations can be determined by NMR for mobile molecular elements such as O-glycosidic linkages with minimal input from theory. The approach also provides an experimental means to validate the conformational preferences predicted from MD simulations. The conformational behaviors of Φ in the 12 disaccharides were very similar, but those of ψ varied significantly, allowing a classification of the 12 disaccharides based on preferred linkage conformation in solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

pmartR 2.0 : A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes

The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry (MS) data, tailored to specific characteristics of proteomic (isobaric or labelled), metabolomic, and lipidomic datasets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance (NMR) metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for some transcriptomic data analyses. These improvements have made progress towards a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package’s statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.

59 BASIC BIOLOGICAL SCIENCES↗

Investigation of the Self-Assembly Behavior of Statistical Bottlebrush Copolymers via Self-Consistent Field Theory Simulations

Bottlebrush copolymers, a class of high-density side-chain-grafted copolymers with high molecular weights, have recently attracted extensive research attention due to the dramatic reduction of chain entanglement in these copolymers, unique self-assembly behaviors, and distinctive physical properties. In this study, we employ self-consistent field theory simulations to systemically investigate the phase behavior of statistical bottlebrush copolymers A-stat-B, an intriguing subclass of bottlebrush copolymers with random or alternating sequences of A and B side chains. We find that a broader variety of ordered mesophases can be stabilized in the melts of these copolymers relative to the more extensively studied bottlebrush block copolymers. In particular, we observe that these statistical copolymers can stabilize sphere phases over a larger range of species volume fractions than their diblock and tetrablock counterparts but require much higher segregation strength to achieve this. The sphere and hexagonally packed cylinder phases formed by conformationally symmetric statistical bottlebrush copolymers possess interesting discrete core–shell structures with the backbone and lower volume fraction side-chain species forming shells and cores, respectively. Moreover, we uncover a new strategy of stabilizing the Frank–Kasper phase A15 by introducing conformational asymmetry through different side-chain lengths and also observe deflection of order–order phase boundaries and significant mixing between the backbones and short side chains as conformational asymmetry is introduced.

36 MATERIALS SCIENCE↗

Understanding Differences in California Climate Projections Produced by Dynamical and Statistical Downscaling

Abstract We compare historical and end‐of‐century temperature and precipitation patterns over California from one dynamically downscaled simulation using the Weather Research and Forecast (WRF) model and two simulations statistically downscaled using Localized Constructed Analogs (LOCA). We uniquely separate causes of differences between dynamically and statistically based future climate projections into differences in historical climate (gridded observations versus regional climate model output) and differences in how these downscaling techniques explicitly handle future climate changes (numerical modeling versus analogs). In these methods, solutions between different downscaling techniques differ more in the future compared to the historical period. Changes projected by LOCA are insensitive to the choice of driving data. Only through dynamical downscaling can we simulate physically consistent regional springtime warming patterns across the Sierra Nevada, while the statistical simulations inherit an unphysical signal from their parent Global Climate Model (GCM) or gridded data. The results of our study clarify why these different techniques produce different outcomes and may also provide guidance on which downscaled products to use for certain impact analyses in California and perhaps other Mediterranean regimes.

54 ENVIRONMENTAL SCIENCES↗

Superior Daily and Sub-Daily Precipitation Statistics for Intense and Long-Lived Storms in Global Storm-Resolving Models

Here, daily and sub-daily precipitation statistics are investigated from three global model ensembles: (a) global storm-resolving models (GSRMs) with typical horizontal resolutions of ~4 km, (b) “high”-resolution global models with typical resolutions of ~50 km and (c) “standard”-resolution global models with typical resolutions of ~100 km. Compared to two satellite rainfall datasets, GSRMs convincingly exhibit superior performance for statistics of heavier rain rate events including their diurnal cycle, spatial propagation and the amount contributed by intense precipitation, but not for statistics of weaker or shorter duration precipitation. Both high- and standard-resolution models fail to simulate the correct phase and amplitude of diurnal cycle of precipitation and the propagating convection in the Central US, but high-resolution models show relative improvement in the distribution of precipitation frequency and amount, especially for intense precipitation.

54 ENVIRONMENTAL SCIENCES↗

The Potential Benefits of Handling Mixture Statistics via a Bi-Gaussian EnKF: Tests With All-Sky Satellite Infrared Radiances

The meteorological characteristics of cloudy atmospheric columns can be very different from their clear counterparts. Thus, when a forecast ensemble is uncertain about the presence/absence of clouds at a specific atmospheric column (i.e., some members are clear while others are cloudy), that column's ensemble statistics will contain a mixture of clear and cloudy statistics. Such mixtures are inconsistent with the ensemble data assimilation algorithms currently used in numerical weather prediction. Hence, ensemble data assimilation algorithms that can handle such mixtures can potentially outperform currently used algorithms. In this study, we demonstrate the potential benefits of addressing such mixtures through a bi-Gaussian extension of the ensemble Kalman filter (BGEnKF). The BGEnKF is compared against the commonly used ensemble Kalman filter (EnKF) using perfect model observing system simulated experiments (OSSEs) with a realistic weather model (the Weather Research and Forecast model). Synthetic all-sky infrared radiance observations are assimilated in this study. In these OSSEs, the BGEnKF outperforms the EnKF in terms of the horizontal wind components, temperature, specific humidity, and simulated upper tropospheric water vapor channel infrared brightness temperatures. This study is one of the first to demonstrate the potential of a Gaussian mixture model EnKF with a realistic weather model. Our results thus motivate future research toward improving numerical Earth system predictions though explicitly handling mixture statistics.

54 ENVIRONMENTAL SCIENCES↗

A Non‐Intrusive Machine Learning Framework for Debiasing Long‐Time Coarse Resolution Climate Simulations and Quantifying Rare Events Statistics

Abstract Due to the rapidly changing climate, the frequency and severity of extreme weather is expected to increase over the coming decades. As fully‐resolved climate simulations remain computationally intractable, policy makers must rely on coarse‐models to quantify risk for extremes. However, coarse models suffer from inherent bias due to the ignored “sub‐grid” scales. We propose a framework to non‐intrusively debias coarse‐resolution climate predictions using neural‐network (NN) correction operators. Previous efforts have attempted to train such operators using loss functions that match statistics. However, this approach falls short with events that have longer return period than that of the training data, since the reference statistics have not converged. Here, the scope is to formulate a learning method that allows for correction of dynamics and quantification of extreme events with longer return period than the training data. The key obstacle is the chaotic nature of the underlying dynamics. To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged toward that reference. The method is demonstrated on debiasing an under‐resolved quasi‐geostrophic model and the Energy Exascale Earth System Model (E3SM). For the former, our method enables the quantification of events that have return period two orders longer than the training data. For the latter, when trained on 8 years of ERA5 data, our approach is able to correct the coarse E3SM output to closely reflect the 36‐year ERA5 statistics for all prognostic variables and significantly reduce their spatial biases.

Barthel Sorensen, B.↗

Observation of the modification of quantum statistics of plasmonic systems

Abstract For almost two decades, researchers have observed the preservation of the quantum statistical properties of bosons in a large variety of plasmonic systems. In addition, the possibility of preserving nonclassical correlations in light-matter interactions mediated by scattering among photons and plasmons stimulated the idea of the conservation of quantum statistics in plasmonic systems. It has also been assumed that similar dynamics underlie the conservation of the quantum fluctuations that define the nature of light sources. So far, plasmonic experiments have been performed in nanoscale systems in which complex multiparticle interactions are restrained. Here, we demonstrate that the quantum statistics of multiparticle systems are not always preserved in plasmonic platforms and report the observation of their modification. Moreover, we show that optical near fields provide additional scattering paths that can induce complex multiparticle interactions. Remarkably, the resulting multiparticle dynamics can, in turn, lead to the modification of the excitation mode of plasmonic systems. These observations are validated through the quantum theory of optical coherence for single- and multi-mode plasmonic systems. Our findings unveil the possibility of using multiparticle scattering to perform exquisite control of quantum plasmonic systems.

36 MATERIALS SCIENCE↗

Decoding defect statistics from diffractograms via machine learning

Abstract Diffraction techniques can powerfully and nondestructively probe materials while maintaining high resolution in both space and time. Unfortunately, these characterizations have been limited and sometimes even erroneous due to the difficulty of decoding the desired material information from features of the diffractograms. Currently, these features are identified non-comprehensively via human intuition, so the resulting models can only predict a subset of the available structural information. In the present work we show (i) how to compute machine-identified features that fully summarize a diffractogram and (ii) how to employ machine learning to reliably connect these features to an expanded set of structural statistics. To exemplify this framework, we assessed virtual electron diffractograms generated from atomistic simulations of irradiated copper. When based on machine-identified features rather than human-identified features, our machine-learning model not only predicted one-point statistics (i.e. density) but also a two-point statistic (i.e. spatial distribution) of the defect population. Hence, this work demonstrates that machine-learning models that input machine-identified features significantly advance the state of the art for accurately and robustly decoding diffractograms.

36 MATERIALS SCIENCE↗

Inter‐area oscillation statistical analysis of the U.S. Eastern interconnection

Although extensive studies have been conducted on inter‐area oscillation detection and analysis by using phasor measurement data, few have focused on inter‐area oscillation statistics due to the lack of considerably long‐term and high‐resolution recorded data. A preliminary statistical analysis on the inter‐area oscillations that occurred in the U.S. Eastern interconnection from 2010 to 2015 is presented. These oscillation events were captured and recorded by the wide‐area frequency monitoring network FNET/GridEye. Oscillation occurrence, dominant frequency, damping ratio and oscillation excitation types are investigated to explore the statistical characteristics of the detected inter‐area oscillations. The results from this paper will provide valuable information for system operators and planners regarding network operation and future grid development.

Cui, Yi↗

Electron transfer statistics and thermal fluctuations in molecular junctions

We derive analytical expressions for probability distribution function (PDF) for electron transport in a simple model of quantum junction in presence of thermal fluctuations. Our approach is based on the large deviation theory combined with the generating function method. For large number of electrons transferred, the PDF is found to decay exponentially in the tails with different rates due to applied bias. This asymmetry in the PDF is related to the fluctuation theorem. Statistics of fluctuations are analyzed in terms of the Fano factor. Thermal fluctuations play a quantitative role in determining the statistics of electron transfer; they tend to suppress the average current while enhancing the fluctuations in particle transfer. This gives rise to both bunching and antibunching phenomena as determined by the Fano factor. The thermal fluctuations and shot noise compete with each other and determine the net (effective) statistics of particle transfer. Exact analytical expression is obtained for delay time distribution. The optimal values of the delay time between successive electron transfers can be lowered below the corresponding shot noise values by tuning the thermal effects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Lagrangian statistics in weakly forced two-dimensional turbulence

Measurements of Lagrangian single-point and multiple-point statistics in a quasi-two-dimensional stratified layer system are reported. The system consists of a layer of salt water over an immiscible layer of Fluorinert and is forced electromagnetically so that mean-squared vorticity is injected at a well-defined spatial scale r i . Simultaneous cascades develop in which enstrophy flows predominately to small scales whereas energy cascades, on average, to larger scales. Lagrangian correlations and one- and two-point displacements are measured for random initial conditions and for initial positions within topological centers and saddles. Some of the behavior of these quantities can be understood in terms of the trapping characteristics of long-lived centers, the slow motion near strong saddles, and the rapid fluctuations outside of either centers or saddles. We also present statistics of Lagrangian velocity fluctuations using energy spectra in frequency space and structure functions in real space. We compare with complementary Eulerian velocity statistics. We find that simultaneous inverse energy and enstrophy ranges present in spectra are not directly echoed in real-space moments of velocity difference. Nevertheless, the spectral ranges line up well with features of moment ratios, indicating that although the moments are not exhibiting unambiguous scaling, the behavior of the probability distribution functions is changing over short ranges of length scales. Furthermore, implications for understanding weakly forced 2D turbulence with simultaneous inverse and direct cascades are discussed.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Gaussian process based optimization of molecular geometries using statistically sampled energy surfaces from quantum Monte Carlo

Optimization of atomic coordinates and lattice parameters remains a significant challenge to the wide use of stochastic electronic structure methods such as quantum Monte Carlo (QMC). Measurements of the forces and stress tensor by these methods contain statistical errors, challenging conventional gradient-based numerical optimization methods that assume deterministic results. Additionally, forces are not yet available for some methods, wavefunctions, and basis sets and when available may be expensive to compute to sufficiently high statistical accuracy near energy minima, where the energy surfaces are flat. Here, we explore the use of Gaussian process based techniques to sample the energy surfaces and reduce sensitivity to the statistical nature of the problem. We utilize Latin hypercube sampling, with the number of sampled energy points scaling quadratically with the number of optimized parameters. Furthermore, we show these techniques may be successfully applied to systems consisting of tens of parameters, demonstrating QMC optimization of a benzene molecule starting from a randomly perturbed, broken symmetry geometry.

74 ATOMIC AND MOLECULAR PHYSICS↗

Fully versus constrained statistical fragmentation of carbon clusters and their heteronuclear derivatives

The Microcanonical Metropolis Monte Carlo (MMMC) method has been shown to describe reasonably well fragmentation of clusters composed of identical atomic species. However, this is not so clear in the case of heteronuclear clusters as some regions of phase space might be inaccessible due to the different mobility of the different atomic species, the existence of large isomerization barriers, or the quite different chemical nature of the possible intermediate species. In this paper, we introduce a constrained statistical model that extends the range of applicability of the MMMC method to such mixed clusters. The method is applied to describe fragmentation of isolated clusters with high, moderate, and no heteronuclear character, namely, C n H m , C n N, and C n clusters for which experimental fragmentation branching ratios are available in the literature. We show that the constrained statistical model describes fairly well fragmentation of CnHm clusters in contrast with the poor description provided by the fully statistical model. The latter model, however, works pretty well for both C n and C n N clusters, thus showing that the ultimate reason for this discrepancy is the inability of the MMMC method to selectively explore the whole phase space. This conclusion has driven us to predict the fragmentation patterns of the C 4 N cluster for which experiments are not yet available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Magnetic imaging and statistical analysis of the metamagnetic phase transition of FeRh with electron spins in diamond

Magnetic imaging based on nitrogen-vacancy (NV) centers in diamond has emerged as a powerful tool for probing magnetic phenomena in fields ranging from biology to physics. A key strength of NV sensing is its local-probe nature, enabling high-resolution spatial images of magnetic stray fields emanating from a sample. However, this local character can also form a drawback for analyzing the global properties of a system, such as a phase transition temperature. Here, we address this challenge by using statistical analyses of magnetic-field maps to characterize the first-order temperature-driven metamagnetic phase transition from the antiferromagnetic to the ferromagnetic state in FeRh. After imaging the phase transition and identifying the regimes of nucleation, growth, and coalescence of ferromagnetic domains, we statistically characterize the spatial magnetic-field maps to extract the transition temperature and thermal hysteresis width. By analyzing the spatial correlations of the maps in relation to the magnetocrystalline anisotropy and external magnetic field, we detect a reorientation of domain walls across the phase transition. The employed statistical approach can be extended to the study of other magnetic phenomena with NV magnetometry or other sensing techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

On the statistical theory of self-gravitating collisionless dark matter flow: High order kinematic and dynamic relations

Dark matter, if it exists, accounts for five times as much as ordinary baryonic matter. To better understand the self-gravitating collisionless dark matter flow on different scales, a statistical theory involving kinematic and dynamic relations must be developed for different types of flow, e.g., incompressible, constant divergence, and irrotational flow. This is mathematically challenging because of the intrinsic complexity of dark matter flow and the lack of a self-closed description of flow velocity. Here, this paper extends our previous work on second-order statistics Xu to kinematic relations of any order for any type of flow. Dynamic relations were also developed to relate statistical measures of different orders. The results were validated by N-body simulations. On large scales, we found that (i) third-order velocity correlations can be related to density correlation or pairwise velocity; (ii) the pth-order velocity correlations follow ∝ a (p+2)/2 for odd p and ∝ a p/2 for even p, where a is the scale factor; (iii) the overdensity δ is proportional to density correlation on the same scale, $\langle$δ$\rangle$∝$\langle$δδ'$\rangle$; (iv) velocity dispersion on a given scale r is proportional to the overdensity on the same scale. On small scales, (i) a self-closed velocity evolution is developed by decomposing the velocity into motion in haloes and motion of haloes; (ii) the evolution of vorticity and enstrophy are derived from the evolution of velocity; (iii) dynamic relations are derived to relate second- and third-order correlations; (iv) while the first moment of pairwise velocity follows $\langle$Δu L $\rangle$=-Har (H is the Hubble parameter), the third moment follows $\langle$(Δu L ) 3 $\rangle$ ∝ ε u ar that can be directly compared with simulations and observations, where ε u ≈ 10 -7 m 2 /s 3 is the constant rate for energy cascade; (v) the pth order velocity correlations follow ∝ a (3p-5)/4 for odd p and ∝ a 3p/4 for even p. Finally, the combined kinematic and dynamic relations lead to exponential and one-fourth power-law velocity correlations on large and small scales, respectively.

79 ASTRONOMY AND ASTROPHYSICS↗

Impact of classical statistics on thermal conductivity predictions of BAs and diamond using machine learning molecular dynamics

Machine learning interatomic potentials (MLIPs) have greatly enhanced molecular dynamics (MD) simulations, achieving near-first-principles accuracy in thermal conductivity studies. In this work, we reveal that this accuracy, observed in BAs and diamond at sub-Debye temperatures, stems from an accidental error cancelation: classical statistics overestimates specific heat while underestimating phonon lifetimes, balancing out in thermal conductivity predictions. However, this balance is disrupted when isotopes are introduced, leading MLIP-based MD to significantly underpredict thermal conductivity compared to experiments and quantum statistics-based Boltzmann transport equation. This discrepancy arises not from classical statistics affecting phonon–isotope scattering rates but from its impact on the interplay between phonon–isotope and phonon–phonon scattering in the normal scattering-dominated BAs and diamond. In conclusion, this work underscores the limitations of MLIP-based MD for thermal conductivity studies at sub-Debye temperatures.

36 MATERIALS SCIENCE↗