Pathfinder for a high statistics search for missing energy in gamma cascades
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72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
SEARCH · Engineering Papers
Results for “statistical physics”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows
In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.
42 ENGINEERING↗
Constraining the dark matter contribution of $\gamma$ rays in clusters of galaxies using Fermi -LAT data
Clusters of galaxies are the largest gravitationally bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high x-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the Fermi-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of 2.5σ-3.0σ when considering a slope for the subhalo mass distribution α =1.9 and minimum mass of M min =10 -6 M ⊙ . The best-fit DM mass and annihilation cross sections for a b$\overline{b}$ annihilation channel are m X =40–60 GeV and $\langle$σν$\rangle$=(2–4)×10 -25 cm 3 /s. When we consider α =2.0 and M min =10 -9 M ⊙ , the best fit of the cross section reduces to $\langle$σν$\rangle$=(4–10)×10 -26 cm 3 /s. For both DM substructure models there is a tension between the values of $\langle$σν$\rangle$ that we find and the upper limits obtained with the nondetection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. In conclusion, this signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields.
79 ASTRONOMY AND ASTROPHYSICS↗
The density distribution and physical origins of intermittency in supersonic, highly magnetized turbulence with diverse modes of driving
The probability density function (PDF) of the logarithmic density contrast, s = ln (ρ/ρ 0 ), with gas density ρ and mean density ρ 0 , for hydrodynamical supersonic turbulence is well known to have significant non-Gaussian (intermittent) features that monotonically increase with the turbulent Mach number, M. By studying the mass- and volume-weighted s-PDF for an ensemble of 36 sub-to-trans-Alfvenic mean-field, supersonic, isothermal turbulence simulations with different modes of driving, relevant to molecular gas in the cool interstellar medium, we show that a more intricate picture emerges for the non-Gaussian nature of s. Using four independent measures of the non-Gaussian components, we find hydrodynamical-like structure in the highly magnetized plasma for M≲4. However, for M≳4, the non-Gaussian signatures disappear, leaving approximately Gaussian s-statistics – exactly the opposite of hydrodynamical turbulence in the high-M limit. We also find that the non-Gaussian components of the PDF increase monotonically with more compressive driving modes. To understand the M≲4 non-Gaussian features, we use one-dimensional pencil beams to explore the dynamics along and across the large-scale magnetic field, B 0 . Here we discuss kinetic, density, and magnetic field fluctuations from the pencil beams, and identify physical sources of non-Gaussian components to the PDF as single, strong shocks coupled to fast magnetosonic compressions that form along B 0 . We discuss the Gaussianization of the M≳4 s-fields through the lens of two phenomenologies: the self-similarity of the s-field and homogenization of the dynamical time-scales between the over- and underdense regions in the compressible gas.
79 ASTRONOMY AND ASTROPHYSICS↗
Combined Neyman–Pearson chi-square: An improved approximation to the Poisson-likelihood chi-square
We describe an approximation to the widely-used Poisson-likelihood chi-square using a linear combination of Neyman’s and Pearson’s chi-squares, namely “combined Neyman–Pearson chi-square” ($X^2_{CNP}$). Through analytical derivations and toy model simulations, we show that leads to a significantly smaller bias on the best-fit model parameters compared to those using either Neyman’s or Pearson’s chi-square. When the computational cost of using the Poisson-likelihood chi-square is high, $X^2_{CNP}$ provides a good alternative given its natural connection to the covariance matrix formalism.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Redshift evolution of the H I detection rate in radio-loud active galactic nuclei
We present a search for associated H I 21 cm absorption in a sample of 29 radio-loud active galactic nuclei (AGNs) at 0.7 < z < 1, carried out with the upgraded Giant Metrewave Radio Telescope. We detect H I 21 cm absorption against none of our target AGNs, obtaining 3σ upper limits to the optical depth of ≲1% per 50 km s –1 channel. The radio luminosity of our sources is lower than that of most AGNs searched for H I 21 cm absorption at similar redshifts in the literature, and, for all targets except two, the UV luminosity is below the threshold 10 23 W Hz –1 , above which the H I in the AGN environment has been suggested to be completely ionised. We stacked the H I 21 cm spectra to obtain a more stringent limit of ≈0.17% per 50 km s –1 channel on the average H I 21 cm optical depth of the sample. Furthermore, the sample is dominated by extended radio sources, 24 of which are extended on scales of tens of kiloparsecs. Including similar extended sources at 0.7 < z < 1.0 from the literature, and comparing with a low-z sample of extended radio sources, we find statistically significant (≈3σ) evidence that the strength of H I 21 cm absorption towards extended radio sources is weaker at 0.7 < z < 1.0 than at z < 0.25, with a lower detection rate of H I 21 cm absorption at 0.7 < z < 1.0. Redshift evolution in the physical conditions of H I is the likely cause of the weaker associated H I 21 cm absorption at high redshifts, due to either a low H I column density or a high spin temperature in high-z AGN environments.
79 ASTRONOMY AND ASTROPHYSICS↗
Finite-temperature many-body perturbation theory for electrons: Algebraic recursive definitions, second-quantized derivation, linked-diagram theorem, general-order algorithms, and grand canonical and canonical ensembles
A comprehensive and detailed account is presented for the finite-temperature many-body perturbation theory for electrons that expands in power series all thermodynamic functions on an equal footing. Algebraic recursions in the style of the Rayleigh–Schrödinger perturbation theory are derived for the grand potential, chemical potential, internal energy, and entropy in the grand canonical ensemble and for the Helmholtz energy, internal energy, and entropy in the canonical ensemble, leading to their sum-over-states analytical formulas at any arbitrary order. For the grand canonical ensemble, these sum-over-states formulas are systematically transformed to sum-over-orbitals reduced analytical formulas by the quantum-field-theoretical techniques of normal-ordered second quantization and Feynman diagrams extended to finite temperature. It is found that the perturbation corrections to energies entering the recursions have to be treated as a nondiagonal matrix, whose off-diagonal elements are generally nonzero within a subspace spanned by degenerate Slater determinants. They give rise to a unique set of linked diagrams—renormalization diagrams—whose resolvent lines are displaced upward, which are distinct from the well-known anomalous diagrams of which one or more resolvent lines are erased. A linked-diagram theorem is introduced that proves the size-consistency of the finite-temperature many-body perturbation theory at any order. General-order algorithms implementing the recursions establish the convergence of the perturbation series toward the finite-temperature full-configuration-interaction limit unless the series diverges. As a result, the normal-ordered Hamiltonian at finite temperature sheds light on the relationship between the finite-temperature Hartree–Fock and first-order many-body perturbation theories.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Transferability of data-driven, many-body models for CO 2 simulations in the vapor and liquid phases
Here, extending on the previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246–2257 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO 2 and systematically assess how the strength and anisotropy of the CO 2 –CO 2 interactions affect the models’ ability to predict vapor, liquid, and vapor–liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting one-body and two-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the charge model 5 scheme to determine the atomic charges and systematically scale the two-body energies to obtain more accurate descriptions of vapor, liquid, and vapor–liquid equilibrium properties. Challenges in model construction arise due to the anisotropic nature and small magnitude of the interaction energies in CO 2 , calling for the necessity of highly accurate descriptions of the multidimensional energy landscape of liquid CO 2 . These findings emphasize the key role played by the training set quality in the development of transferable, data-driven models, which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Potentials of mean force fail to describe chemical bond-breaking in solution
Many liquid phase studies assume that the potential energy surfaces of reacting molecules are the same as in the gas phase, neglecting complex solvent dynamics that can completely alter the nature of chemical reactivity. Even studies that include solvent effects typically only consider them in an average, equilibrium way as part of a potential of mean force (PMF). In this work, we use mixed quantum/classical simulations to compare how equilibrium and non-equilibrium solvent motions affect the photodissociation of a simple diatomic molecule, NaK + , in liquid tetrahydrofuran. A PMF analysis shows that as the excited-state molecule dissociates with the solvent at equilibrium, the bonding electron remains associated with K + at short bond distances but eventually localizes on Na + at the end of dissociation. When we examine non-equilibrium dynamical photodissociation trajectories, however, we find that they fall into three distinct categories: about a quarter of them have the bonding electron mainly associated with Na + , another quarter stay mainly associated with K + , and about half have the bonding electron shared roughly equally between the two ions. The results show that equilibrium PMFs cannot accurately describe the dynamics of bond-breaking chemical reactions in solution because there is insufficient time for the solvent to reach equilibrium on the time scale over which bond dissociation occurs. Furthermore, our analysis shows that the solvent coupling between the electronic energy surfaces is similar at and away from equilibrium, suggesting that other factors, such as solute velocity-driven solvent memory effects, play a more important role in explaining the failure of the equilibrium PMF to predict the non-equilibrium dynamics.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Mana and thermalization: Probing the feasibility of near-Clifford Hamiltonian simulation
Quantum hydrodynamics is the emergent classical dynamics governing transport of conserved quantities in generic strongly interacting quantum systems. Here, recent matrix product operator methods [1,2] have made simulations of quantum hydrodynamics in 1+1D tractable, but they do not naturally generalize to 2+1D or higher, and they offer limited guidance as to the difficulty of simulations on quantum computers. Near-Clifford simulation algorithms are not limited to one dimension, and future error-corrected quantum computers will likely be bottlenecked by non-Clifford operations. We therefore investigate the non-Clifford resource requirements for simulation of quantum hydrodynamics using mana, a resource theory of non-Clifford operations. For infinite-temperature starting states, we find that the mana of subsystems quickly approaches zero, while for starting states with energy above some threshold the mana approaches a nonzero value. Surprisingly, in each case the finite-time mana is governed by the subsystem entropy, not the thermal state mana; we argue that this is because mana is a sensitive diagnostic of finite-time deviations from canonical typicality.
1-dimensional spin chains↗
Bioenergy Feedstock Library 2023 Annual Summary Report
The Bioenergy Feedstock Library (BFL), part of the Biomass Feedstock National User Facility (BFNUF) located at INL, is a physical sample repository and a web-accessible electronic database. The BFL stores physical and chemical characteristics of biomass and waste carbon sources for energy use as well as samples generated from across U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) and U.S. Department of Agriculture funded projects. The objective of this Bioenergy Feedstock Library Annual Summary Report for 2023 is to focus on the updates to (1) publicly available analytical data and equipment tracked through the BFNUF, (2) physical samples available for request, (3) sample and data archival progress from recent BETO-funded projects, and (4) publicly available datasets created upon request from BETO, INL projects, or outside entities compared to the 2022 Bioenergy Feedstock Library Annual Summary Report. This report highlights key statistics and available data and information important for INL, BFL users, academics, and industry.
09 BIOMASS FUELS↗
Dark energy survey year 1 results: Constraining baryonic physics in the Universe
ABSTRACT Measurements of large-scale structure are interpreted using theoretical predictions for the matter distribution, including potential impacts of baryonic physics. We constrain the feedback strength of baryons jointly with cosmology using weak lensing and galaxy clustering observables (3 × 2pt) of Dark Energy Survey (DES) Year 1 data in combination with external information from baryon acoustic oscillations (BAO) and Planck cosmic microwave background polarization. Our baryon modelling is informed by a set of hydrodynamical simulations that span a variety of baryon scenarios; we span this space via a Principal Component (PC) analysis of the summary statistics extracted from these simulations. We show that at the level of DES Y1 constraining power, one PC is sufficient to describe the variation of baryonic effects in the observables, and the first PC amplitude (Q1) generally reflects the strength of baryon feedback. With the upper limit of Q1 prior being bound by the Illustris feedback scenarios, we reach $\sim 20{{\ \rm per\ cent}}$ improvement in the constraint of $S_8=\sigma _8(\Omega _{\rm m}/0.3)^{0.5}=0.788^{+0.018}_{-0.021}$ compared to the original DES 3 × 2pt analysis. This gain is driven by the inclusion of small-scale cosmic shear information down to 2.5 arcmin, which was excluded in previous DES analyses that did not model baryonic physics. We obtain $S_8=0.781^{+0.014}_{-0.015}$ for the combined DES Y1+Planck EE+BAO analysis with a non-informative Q1 prior. In terms of the baryon constraints, we measure $Q_1=1.14^{+2.20}_{-2.80}$ for DES Y1 only and $Q_1=1.42^{+1.63}_{-1.48}$ for DESY1+Planck EE+BAO, allowing us to exclude one of the most extreme AGN feedback hydrodynamical scenario at more than 2σ.
79 ASTRONOMY AND ASTROPHYSICS↗
Unique & challenging aspects of plutonium metal standards exchange program for actinide measurements
The Los Alamos National Laboratory exchange program is the only program of its kind for the distribution of plutonium (Pu) standards materials with a range of impurity contents to multiple laboratories for destructive measurements of elemental concentration. This paper discusses statistical methods used to address challenges in Pu metal exchange data by way of two case studies. Challenges include how to evaluate a data set when a large fraction of the values are minimum detection limits (MDLs), and how to determine potential outliers with limited in-formation on the true spread of the data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Strange Mesons and Gluonic Excitations
In the standard model of particle physics, the interactions between the fundamental constituents of nuclear matter, quarks and gluons, are governed by the theory of Quantum Chromodynamics (QCD). A central goal of nuclear physics is to understand how hadrons, such as protons and neutrons, are formed from these underlying quark and gluon degrees of freedom. A hadron is primarily constructed from three quarks or a quark-antiquark pair; however, the theory of QCD allows for much more exotic configurations. One of the predicted exotic configurations is known as a hybrid meson, which contains an excited gluonic field in addition to the usual quark-antiquark pair. This project aims to search for and study these gluonic excitations using the Gluonic Excitation (GlueX) experiment at Jefferson Lab in Newport News, VA. The discovery potential of the experiment will be significantly extended by studying the quark flavor composition of the meson spectrum through the completion and use of an enhanced detector to identify mesons containing strange quarks. The unprecedented statistical precision of the data collected at GlueX will allow us to search for a pattern of light-quark hybrid mesons, providing new insight into the interactions that bind the fundamental quarks and gluons into the hadrons we observe in nature.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Unfolding quantum computer readout noise
Abstract In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. An important class of qubit errors are readout errors. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Exploring diversion-pathway analysis of a generic molten-salt fast reactor using multiphysics informed signatures
Molten salt reactors are being explored by multiple commercial ventures due to their inherent safety features, flexibility in fuel sources, and high fuel utilization and thermal efficiency. The continual flow of fuel salt, large fissile quantities present, and ability to add or divert material due to the liquid nature introduces new challenges for international safeguards. To understand how international safeguards should be applied, it is important to capture the inherent multi-physics nature of a molten salt reactor. This work examines a generic molten salt fast reactor to understand how potential diversion scenarios would affect the concentration of radionuclides in the primary and auxiliary systems. Three types of diversion were examined: a slow drip of fuel salt, gaseous plutonium extraction, and uranium metal plating. The analysis determined that several key isotopes become statistically significant once diversion begins, indicating that detection of such diversion cases would be possible through measuring specific signatures such as gamma spectra.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Origin of nontopological soliton dark matter: solitosynthesis or phase transition
This work demonstrates that nontopological solitons with large global charges and masses, even above the Planck scale, can form in the early universe and dominate the dark matter abundance. In solitosynthesis, solitons prefer to grow as large as possible under equilibrium dynamics when an initial global charge asymmetry is present. Their abundance is set by when soliton formation via particle fusion freezes out, and their charges are set by the time it takes to accumulate free particles. This work improves the estimation of both quantities, and in particular shows that much larger-charged solitons form than previously thought. The results are estimated analytically and validated numerically by solving the coupled Boltzmann equations. Without solitosynthesis, phase transitions can still form solitons from particles left inside false-vacuum pockets and determine their present-day abundance and properties. Even with zero charge asymmetry, solitons formed in this way can have very large charges on account of statistical fluctuations in the numbers of (anti)particles inside each pocket.
79 ASTRONOMY AND ASTROPHYSICS↗
Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation
Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.
74 ATOMIC AND MOLECULAR PHYSICS↗