Higher order conserved charge fluctuations inside the mixed phase
Not Available
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
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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Reconstructing gravity on cosmological scales
We present the data-driven late-time reconstruction of gravitational theories and dark energy models on cosmological scales. We showcase the power of present cosmological probes at constraining these models and quantify the knowledge of their properties that can be acquired through state of the art data. This reconstruction exploits the power of the effective field theory approach to dark energy and modified gravity phenomenology, which compresses the freedom in defining such models into a finite set of functions that can be reconstructed across cosmic times using cosmological data. We consider several model classes described within this framework and thoroughly discuss their phenomenology and data implications. In this work, we find that some models can alleviate the present discrepancy in the determination of the Hubble constant as inferred from the cosmic microwave background and as directly measured. This results in a statistically significant preference for the reconstructed theories over the standard cosmological model.
79 ASTRONOMY AND ASTROPHYSICS↗
Evidence of the h c → K S 0 K + π − + c . c . decay
Based on ( 2.712 ± 0.014 ) × 10 9 ψ ( 3686 ) events collected by the BESIII Collaboration, evidence of the hadronic decay h c → K S 0 K + π − + c . c . is found with a significance of 4.3 σ in the ψ ( 3686 ) → π 0 h c process. The branching fraction of h c → K S 0 K + π − + c . c . is measured to be ( 7.3 ± 1.8 ± 0.8 ) × 10 − 4 , where the first and second uncertainties are statistical and systematic, respectively. Combining with the exclusive decay width of η c → K K ¯ π , our result indicates inconsistencies with both pQCD and NRQCD predictions. Published by the American Physical Society 2024
Ablikim, M.↗
Floquet Prethermalization with Lifetime Exceeding 90 s in a Bulk Hyperpolarized Solid
Here, we report the observation of long-lived Floquet prethermal states in a bulk solid composed of dipolar-coupled 13 C nuclei in diamond at room temperature. For precessing nuclear spins prepared in an initial transverse state, we demonstrate pulsed spin-lock Floquet control that prevents their decay over multiple-minute-long periods. We observe Floquet prethermal lifetimes $T^{'}_{2}$≈90.9 s, extended >60 000-fold over the nuclear free induction decay times. The spins themselves are continuously interrogated for ~10 min, corresponding to the application of ≈5.8×10 6 control pulses. The 13 C nuclei are optically hyperpolarized by lattice nitrogen vacancy centers; the combination of hyperpolarization and continuous spin readout yields significant signal-to-noise ratio in the measurements. This allows probing the Floquet thermalization dynamics with unprecedented clarity. We identify four characteristic regimes of the thermalization process, discerning short-time transient processes leading to the prethermal plateau and long-time system heating toward infinite temperature. This Letter points to new opportunities possible via Floquet control in networks of dilute, randomly distributed, low-sensitivity nuclei. In particular, the combination of minutes-long prethermal lifetimes and continuous spin interrogation opens avenues for quantum sensors constructed from hyperpolarized Floquet prethermal nuclei.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter
Time-reversal symmetry breaking and entropy production are universal features of nonequilibrium phenomena. Despite its importance in the physics of active and living systems, the entropy production of systems with many degrees of freedom has remained of little practical significance because the high dimensionality of their state space makes it difficult to measure. Here, in this work, we introduce a local measure of entropy production and a numerical protocol to estimate it. We establish a connection between the entropy production and extractability of work in a given region of the system and show how this quantity depends crucially on the degrees of freedom being tracked. We validate our approach in theory, simulation, and experiments by considering systems of active Brownian particles undergoing motility-induced phase separation, as well as active Brownian particles and E.coli in a rectifying device in which the time-reversal asymmetry of the particle dynamics couples to spatial asymmetry to reveal its effects on a macroscopic scale.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Statistical detection of Josephson, Andreev, and single quasiparticle currents in scanning tunneling microscopy
Not Available
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Analysis Report documenting the Assessment of the Solubility of Lead, EDTA and other Organic Ligands in non-Sulfide systems performed under TP 08-02 and under TP 20-01
The objective of this report is to accept or reject the hypothesis that the experiments conducted under TP 08-02 Revision 0 (Ismail et al., 2008) were affected by CO 2 (g) intrusion and sample contamination. The test of the hypothesis is accomplished by comparing the experimental data collected under the protocols of TP 08-02 Revision O and TP 20-01 Revision O (Kirkes and Zhang, 2020). The protocols of TP 20-01 Revision 0 minimize the possibilities of CO 2 (g) intrusion and sample contamination. The experimental data sets obtained under both TPs will be assessed statistically to see if they are identical or not.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A Statistical Mechanics Treatment of Multiconfigurational Ground States in Isolated Neutral Atoms [Presentations]
Abstract not provided.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble Kalman filter (MFEnKF) recently developed by the authors combines a full-order physical model and a hierarchy of reduced order surrogate models in order to increase the computational efficiency of data assimilation. The standard MFEnKF uses linear couplings between models, and is statistically optimal in case of Gaussian probability densities. This work extends the MFEnKF into to make use of a broader class of surrogate model such as those based on machine learning methods such as autoencoders non-linear couplings in between the model hierarchies. We identify the right-invertibility property for autoencoders as being a key predictor of success in the forecasting power of autoencoder-based reduced order models. We propose a methodology that allows us to construct reduced order surrogate models that are more accurate than the ones obtained via conventional linear methods. Numerical experiments with the canonical Lorenz'96 model illustrate that nonlinear surrogates perform better than linear projection-based ones in the context of multifidelity ensemble Kalman filtering. We additionality show a large-scale proof-of-concept result with the quasi-geostrophic equations, showing the competitiveness of the method with a traditional reduced order model-based MFEnKF.
97 MATHEMATICS AND COMPUTING↗
Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys
Abstract From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens class of any single event. We investigate this method’s effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of 30 M ⊙ . On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ≈25%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A Catalog of Candidate Double and Lensed Quasars from Gaia and WISE Data
Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia Early Data Release 3 (EDR3), we generate a catalog of candidate double- and multiply imaged lensed quasars and active galactic nuclei (AGNs), comprising 3140 systems. It includes two partially overlapping parts: a sample of distant (redshifts mostly greater than 1) sources with perturbed data; and systems that have been resolved into separate components by Gaia at separations less than 2''. For the first part, which is roughly one-third of the published catalog, we synthesized 0.617 million redshifts using multiple machine-learning prediction and classification methods, using independent photometric and astrometric data from Gaia EDR3 and the Wide-field Infrared Survey Explorer, with accurate spectroscopic redshifts from the Sloan Digital Sky Survey (SDSS) as a training set. Using these synthetic redshifts, we estimate a 4.9% rate of interlopers with spectroscopic redshifts below 1 in this part of the catalog. Unresolved candidate double and dual AGNs and quasars are selected as sources with a marginally high BP/RP excess factor (phot_bp_rp_excess_factor), which is sensitive to source extent, limiting our search to high-redshift quasars. For the second part of the catalog, additional filters on measured parallax and near-neighbor statistics are applied to diminish the propagation of the remaining stellar contaminants. The estimated rate of the positives (double or multiple sources) is 98%, and the estimated rate of dual (physically related) quasars is greater than 54%. A few dozen serendipitously found objects of interest are discussed in more detail, including known and new lensed images, planetary nebulae, young IR stars of peculiar morphology, and quasars with catastrophic redshift errors in SDSS.
79 ASTRONOMY AND ASTROPHYSICS↗
Physics-Informed Recurrent Neural Networks to Predict Reactor Operations of the AGN-201 Nuclear Reactor
4 page paper submitted to ANS Student conference. Summary of paper similar to the following abstract: The ability to predict how a reactor will operate, understand when anomalous conditions arise, and ensure a reactor is being operated as expected is crucial for deploying new nuclear facilities. Digital twins serve as a unique solution to recognizing reactor behavior; however, they require data to be useful. For next-generation reactors, this data may not currently be available. To explore how synthetic physics-informed reactor data can be used to predict reactor operations, a recurrent neural network was implemented for the Idaho State University AGN-201 digital twin. The goal of this work is to determine how synthetic data can be used to train a recurrent neural network model for predicting the reactor power of the AGN-201. The recurrent neural network was validated using both synthetic and real operational data. We envision this approach will help bridge the gap between the virtual and physical sides of a digital twin, where reactor physics models based on as-built data can be corrected for actual operating parameters to ensure the virtual model mirrors reality.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Di-nucleons do not form bound states at heavy pion mass
We perform a high-statistics lattice QCD calculation of the low-energy two-nucleon scattering amplitudes. In order to address discrepancies in the literature, the calculation is performed at a heavy pion mass in the limit that the light quark masses are equal to the physical strange quark mass, $m_π= m_K \simeq 714 $ MeV. Using a state-of-the-art momentum space method, we rule out the presence of a bound di-nucleon in both the isospin 0 (deuteron) and 1 (di-neutron) channels, in contrast with many previous results that made use of compact hexaquark creation operators. In order to diagnose the discrepancy, we add such hexaquark interpolating operators to our basis and find that they do not affect the determination of the two-nucleon finite volume spectrum, and thus they do not couple to deeply bound di-nucleons that are missed by the momentum-space operators. Further, we perform a high-statistics calculation of the HAL QCD potential on the same gauge ensembles and find qualitative agreement with our main results. We conclude that two-nucleons do not form bound states at heavy pion masses and that previous identification of deeply bound di-nucleons must have arisen from a misidentification of the spectrum from off-diagonal elements of a correlation function.
FOS: Physical sciences↗
Neutrino Scattering Measurements on Hydrogen and Deuterium: A Snowmass White Paper
Neutrino interaction uncertainties are a limiting factor in current and next-generation experiments probing the fundamental physics of neutrinos, a unique window on physics beyond the Standard Model. Neutrino-nucleon scattering amplitudes are an important part of the neutrino interaction program. However, since all modern neutrino detectors are composed primarily of heavy nuclei, knowledge of elementary neutrino-nucleon amplitudes relies heavily on experiments performed in the 1970s and 1980s, whose statistical and systematic precision are insufficient for current needs. In this white paper, we outline the motivation for attempting measurements on hydrogen and deuterium that would improve this knowledge, and we discuss options for making these measurements either with the DUNE near detector or with a dedicated facility.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Kinetics of simultaneous hydrodesulfurization and hydrodenitrogenation reactions using CoMoP/Al 2 O 3 and NiMoP/Al 2 O 3
Hydrodesulfurization (HDS) is a key reaction to achieve diesel production at the specified low sulfur levels and is highly affected by a competing reaction involving nitrogen removal through hydrodenitrogenation (HDN). This work evaluated kinetic parameters of simultaneous reactions of HDS of dibenzothiophene (DBT) and HDN of quinoline (Q) using CoMoP/Al 2 O 3 and NiMoP/Al 2 O 3 catalysts under operational conditions that allow a wide range of reagent conversions. Estimated parameters were evaluated using rigorous statistical 2 analysis. Good fits for the evaluated experimental data were provided by both power-law and Langmuir-Hinshelwood models. Further, turnover frequency values highlight adsorption and competition effects between nitrogen-containing compounds and sulfur-containing compounds. NiMoP catalyst showed higher hydrogenating power than CoMoP, with larger absolute value of the estimated adsorption enthalpy (-120 kJ.mol -1 for NiMoP and -75 kJ.mol -1 for CoMoP), suggesting strong adsorption of nitrogen compounds. A catalyst with more hydrogenating power is also more capable of performing both HDN and HDS reactions simultaneously.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
An Energy-Integrated Analysis for Measuring the Anomalous Precession Frequenzc for the Muon g - 2 Experiment at Fermilab
In the search for physics beyond the Standard Model, the Muon g - 2 Experiment at Fermilab (E989) will make the most precise measurement of the anomalous magnetic moment of the muon, a μ . Improvements in precision come from both increased statistics and new techniques to significantly reduce previous systematic uncertainties. The muon a μ is determined by extracting both the anomalous spin precession frequency, ω a , and the average magnetic field sampled by the muons, B. Traditionally an energy threshold analysis method which requires reconstruction of decay positrons from the muon decay, μ + → e + ν e ν μ has been used to extract ω a . This thesis will describe a novel, energy-integrated analysis method for ωa extraction that does not require full reconstruction of positron hits and lowers the energy threshold therefore sampling more of the stored muons. This new technique has different sensitivities to effects like gain fluctuations and positron pulse pile-up. The analysis results will focus on data taken in 2019, the second year of running the experiment.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning
An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.
42 ENGINEERING↗
A probabilistic graphical model foundation for enabling predictive digital twins at scale
A unifying mathematical formulation is needed to move from one-off digital twins built through custom implementations to robust digital twin implementations at scale. This work proposes a probabilistic graphical model as a formal mathematical representation of a digital twin and its associated physical asset. We create an abstraction of the asset–twin system as a set of coupled dynamical systems, evolving over time through their respective state spaces and interacting via observed data and control inputs. The formal definition of this coupled system as a probabilistic graphical model enables us to draw upon well-established theory and methods from Bayesian statistics, dynamical systems and control theory. The declarative and general nature of the proposed digital twin model make it rigorous yet flexible, enabling its application at scale in a diverse range of application areas. Here, we demonstrate how the model is instantiated to enable a structural digital twin of an unmanned aerial vehicle (UAV). The digital twin is calibrated using experimental data from a physical UAV asset. Its use in dynamic decision-making is then illustrated in a synthetic example where the UAV undergoes an in-flight damage event and the digital twin is dynamically updated using sensor data. The graphical model foundation ensures that the digital twin calibration and updating process is principled, unified and able to scale to an entire fleet of digital twins.
42 ENGINEERING↗