Physics

The data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present paper offers a probabilistic perspective that simultaneously identifies predictive, lower-dimensional coarse-grained (CG) variables as well as their dynamics. We make use of the expressive ability of deep neural networks in order to represent the right-hand side of the CG evolution law. Furthermore, we demonstrate how domain knowledge that is very often available in the form of physical constraints (e.g. conservation laws) can be incorporated with the novel concept of virtual observables. Such constraints, apart from leading to physically realistic predictions, can significantly reduce the requisite amount of training data which enables reducing the amount of required, computationally expensive multiscale simulations (Small Data regime). The proposed state-space model is trained using probabilistic inference tools and, in contrast to several other techniques, does not require the prescription of a fine-to-coarse (restriction) projection nor time-derivatives of the state variables. The formulation adopted is capable of quantifying the predictive uncertainty as well as of reconstructing the evolution of the full, fine-scale system which allows to select the quantities of interest a posteriori. We demonstrate the efficacy of the proposed framework in a high-dimensional system of moving particles.

Infrared combination bands of the polar isomer of the N2O dimer are observed for the first time, using a tunable infrared laser source to probe a pulsed slit-jet supersonic expansion in the N2O nu1 region (~2240 cm-1). One band involves the torsional (out-of-plane) intermolecular mode and yields a torsional frequency of 19.83 cm-1 if associated with the out-of-phase fundamental (N2O nu1) vibration of the N2O monomers in the dimer. The other band, which is highly perturbed, yields an intermolecular in-plane geared bend frequency of 22.74 cm-1. The results are compared with high level ab initio calculations. The less likely alternate assignment to the in-phase fundamental would give torsional and geared bend frequencies of 17.25 and 20.16 cm-1, respectively.

The African Network of Women in Astronomy and STEM for GIRLS in Ethiopia initiatives have been established with aim to strengthen the participation of girls and women in astronomy and science in Africa and Ethiopia. We will not be able to achieve the UN Sustainable Development Goals without full participation of women and girls in all aspects of our society and without giving in future the same opportunity to all children to access education independently on their socio-economical status. In this paper both initiatives are briefly introduced.

We designed, constructed and have been operating a system based on single-crystal synthetic diamond sensors, to monitor the beam losses at the interaction region of the SuperKEKB asymmetric-energy electron-positron collider. The system records the radiation dose-rates in positions close to the inner detectors of the Belle II experiment, and protects both the detector and accelerator components against destructive beam losses, by participating in the beam-abort system. It also provides complementary information for the dedicated studies of beam-related backgrounds. We describe the performance of the system during the commissioning of the accelerator and during the first physics data taking.

Inertial particles advected by a background flow can show complex structures. We consider inertial particles in a 2D Taylor-Green (TG) flow and characterize particle dynamics as a function of the particle's Stokes number using dynamic mode decomposition (DMD) method from particle image velocimetry (PIV) like-data. We observe the formation of caustic structures and analyze them using DMD to (a) determine the Stokes number of the particles, and (b) estimate the particle Stokes number composition. Our analysis in this idealized flow will provide useful insight to analyze inertial particles in more complex or turbulent flows. We propose that the DMD technique can be used to perform a similar analysis on an experimental system.

We present an ensemble prediction system using a Deep Learning Weather Prediction (DLWP) model that recursively predicts key atmospheric variables with six-hour time resolution. This model uses convolutional neural networks (CNNs) on a cubed sphere grid to produce global forecasts. The approach is computationally efficient, requiring just three minutes on a single GPU to produce a 320-member set of six-week forecasts at 1.4° resolution. Ensemble spread is primarily produced by randomizing the CNN training process to create a set of 32 DLWP models with slightly different learned weights. Although our DLWP model does not forecast precipitation, it does forecast total column water vapor, and it gives a reasonable 4.5-day deterministic forecast of Hurricane Irma. In addition to simulating mid-latitude weather systems, it spontaneously generates tropical cyclones in a one-year free-running simulation. Averaged globally and over a two-year test set, the ensemble mean RMSE retains skill relative to climatology beyond two-weeks, with anomaly correlation coefficients remaining above 0.6 through six days. Our primary application is to subseasonal-to-seasonal (S2S) forecasting at lead times from two to six weeks. Current forecast systems have low skill in predicting one- or 2-week-average weather patterns at S2S time scales. The continuous ranked probability score (CRPS) and the ranked probability skill score (RPSS) show that the DLWP ensemble is only modestly inferior in performance to the European Centre for Medium Range Weather Forecasts (ECMWF) S2S ensemble over land at lead times of 4 and 5-6 weeks. At shorter lead times, the ECMWF ensemble performs better than DLWP.

The present constraint on the space parity ( P ) and time reflection invariance ( T ) violating electron electric dipole moment ( e EDM) is based on the observation of the electron spin precession in an external electric field using the ThO molecule. We propose an alternative approach: observation of the P ,~ T -odd Faraday effect in an external electric field using the cavity-enhanced polarimetric scheme in combination with a molecular beam crossing the cavity. Our theoretical simulation of the proposed experiment with the PbF and ThO molecular beams shows that the present constraint on the e EDM in principle can be improved by a few orders of magnitude.

Brain tissue deformation resulting from head impacts is primarily caused by rotation and can lead to traumatic brain injury. To quantify brain injury risk based on measurements of kinematics on the head, finite element (FE) models and various brain injury criteria based on different factors of these kinematics have been developed, but the contribution of different kinematic factors has not been comprehensively analyzed across different types of head impacts in a data-driven manner. To better design brain injury criteria, the predictive power of rotational kinematics factors, which are different in 1) the derivative order (angular velocity, angular acceleration, angular jerk), 2) the direction and 3) the power (e.g., square-rooted, squared, cubic) of the angular velocity, were analyzed based on different datasets including laboratory impacts, American football, mixed martial arts (MMA), NHTSA automobile crashworthiness tests and NASCAR crash events. Ordinary least squares regressions were built from kinematics factors to the 95\% maximum principal strain (MPS95), and we compared zero-order correlation coefficients, structure coefficients, commonality analysis, and dominance analysis. The angular acceleration, the magnitude, and the first power factors showed the highest predictive power for the majority of impacts including laboratory impacts, American football impacts, with few exceptions (angular velocity for MMA and NASCAR impacts). The predictive power of rotational kinematics in three directions (x: posterior-to-anterior, y: left-to-right, z: superior-to-inferior) of kinematics varied with different sports and types of head impacts.

A century ago Srinivasa Ramanujan - the great self-taught Indian genius of mathematics - died, shortly after returning from Cambridge, UK, where he had collaborated with Godfrey Hardy. Ramanujan contributed numerous outstanding results to different branches of mathematics, like analysis and number theory, with a focus on special functions and series. Here we refer to apparently weird values which he assigned to two simple divergent series, ??n?? n and ??n?? n 3 . These values are sensible, however, as analytic continuations, which correspond to Riemann's ζ -function. Moreover, they have applications in physics: we discuss the vacuum energy of the photon field, from which one can derive the Casimir force, which has been experimentally measured. We also discuss its interpretation, which remains controversial. This is a simple way to illustrate the concept of renormalization, which is vital in quantum field theory.

The usual radiation self-force of a point charge is obtained in a mathematically exact way and it is pointed out to that this does not call forth that the spacetime motion of a point charge obeys the Lorentz--Abraham--Dirac equation.

We updated the agent based Monte Carlo code HERITAGE that simulates human evolution within restrictive environments such as interstellar, sub-light speed spacecraft in order to include the effects of population genetics. We incorporated a simplified -- yet representative -- model of the whole human genome with 46 chromosomes (23 pairs), containing 2110 building blocks that simulate genetic elements (loci). Each individual is endowed with his/her own diploid genome. Each locus can take 10 different allelic (mutated) forms that can be investigated. To mimic gamete production (sperm and eggs) in human individuals, we simulate the meiosis process including crossing-over and unilateral conversions of chromosomal sequences. Mutation of the genetic information from cosmic ray bombardments is also included. In this first paper of a series of two, we use the neutral hypothesis: mutations (genetic changes) have only neutral phenotypic effects (physical manifestations), implying no natural selection on variations. We will relax this assumption in the second paper. Under such hypothesis, we demonstrate how the genetic patrimony of multi-generational crews can be affected by genetic drift and mutations. It appears that centuries-long deep space travels have small but unavoidable effects on the genetic composition/diversity of the traveling populations that herald substantial genetic differentiation on longer time-scales if the annual equivalent dose of cosmic ray radiation is similar to the Earth radioactivity background at sea level. For larger doses, genomes in the final populations can deviate more strongly with significant genetic differentiation that arises within centuries.

Objective: In this work, we set out to investigate the accuracy of direct attenuation correction (AC) in the image domain for the myocardial perfusion SPECT imaging (MPI-SPECT) using two residual (ResNet) and UNet deep convolutional neural networks. Methods: The MPI-SPECT 99mTc-sestamibi images of 99 participants were retrospectively examined. UNet and ResNet networks were trained using SPECT non-attenuation corrected images as input and CT-based attenuation corrected SPECT images (CT-AC) as reference. The Chang AC approach, considering a uniform attenuation coefficient within the body contour, was also implemented. Quantitative and clinical evaluation of the proposed methods were performed considering SPECT CT-AC images of 19 subjects as reference using the mean absolute error (MAE), structural similarity index (SSIM) metrics, as well as relevant clinical indices such as perfusion deficit (TPD). Results: Overall, the deep learning solution exhibited good agreement with the CT-based AC, noticeably outperforming the Chang method. The ResNet and UNet models resulted in the ME (count) of ??.99±16.72 and ??.41±11.8 and SSIM of 0.99±0.04 and 0.98±0.05 , respectively. While the Change approach led to ME and SSIM of 25.52±33.98 and 0.93±0.09 , respectively. Similarly, the clinical evaluation revealed a mean TPD of 12.78±9.22 and 12.57±8.93 for the ResNet and UNet models, respectively, compared to 12.84±8.63 obtained from the reference SPECT CT-AC images. On the other hand, the Chang approach led to a mean TPD of 16.68±11.24 . Conclusion: We evaluated two deep convolutional neural networks to estimate SPECT-AC images directly from the non-attenuation corrected images. The deep learning solutions exhibited the promising potential to generate reliable attenuation corrected SPECT images without the use of transmission scanning.

The purpose of this article is to show a different way to calculate the electron transfer between atoms. It can be used as a base for calculating the minimal electricity, showed here by Joules, necessary for the transference of electrons. There is already a Table, in which you find the result of all of the electric transfer necessary in most of the materials. In this article, we tried to generalize the electricity necessary, but if you want the result in a specific field or material, you can search the references and collaborates workers.

Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. Here we develop a Bayesian inversion framework that uses Interferometric Synthetic Aperture Radar (InSAR) surface deformation data to infer the laterally heterogeneous permeability of a transient linear poroelastic model of a confined GW aquifer. The Bayesian solution of this inverse problem takes the form of a posterior probability density of the permeability. Exploring this posterior using classical Markov chain Monte Carlo (MCMC) methods is computationally prohibitive due to the large dimension of the discretized permeability field and the expense of solving the poroelastic forward problem. However, in many partial differential equation (PDE)-based Bayesian inversion problems, the data are only informative in a few directions in parameter space. For the poroelasticity problem, we prove this property theoretically for a one-dimensional problem and demonstrate it numerically for a three-dimensional aquifer model. We design a generalized preconditioned Crank--Nicolson (gpCN) MCMC method that exploits this intrinsic low dimensionality by using a low-rank based Laplace approximation of the posterior as a proposal, which we build scalably. The feasibility of our approach is demonstrated through a real GW aquifer test in Nevada. The inherently two dimensional nature of InSAR surface deformation data informs a sufficient number of modes of the permeability field to allow detection of major structures within the aquifer, significantly reducing the uncertainty in the pressure and the displacement quantities of interest.

Many theoretical studies of the voter model (or variations thereupon) involve order parameters that are population-averaged. While enlightening, such quantities may obscure important statistical features that are only apparent on the level of the individual. In this work, we ask which factors contribute to a single voter maintaining a long-term statistical bias for one opinion over the other in the face of social influence. To this end, a modified version of the network voter model is proposed, which also incorporates quenched disorder in the interaction strengths between individuals and the possibility of antagonistic relationships. We find that a sparse interaction network and heterogeneity in interaction strengths give rise to the possibility of arbitrarily long-lived individual biases, even when there is no population-averaged bias for one opinion over the other. This is demonstrated by calculating the eigenvalue spectrum of the weighted network Laplacian using the theory of sparse random matrices.

We present a model for electron-ion transport in Warm Dense Matter that incorporates Coulomb coupling effects into the quantum Boltzmann equation of Uehling and Uhlenbeck through the use of a statistical potential of mean force. Although this model has been derived rigorously in the classical limit [S.D. Baalrud and J. Daligault, Physics of Plasmas 26, 8, 082106 (2019)], its quantum generalization is complicated by the uncertainty principle. Here we apply an existing model for the potential of mean force based on the quantum Ornstein-Zernike equation coupled with an average-atom model [C. E. Starrett, High Energy Density Phys. 25, 8 (2017)]. This potential contains correlations due to both Coulomb coupling and exchange, and the collision kernel of the kinetic theory enforces Pauli blocking while allowing for electron diffraction and large-angle collisions. By solving the Uehling-Uhlenbeck equation for electron-ion relaxation rates, we predict the momentum and temperature relaxation time and electrical conductivity of solid density aluminum plasma based on electron-ion collisions. We present results for density and temperature conditions that span the transition from classical weakly-coupled plasma to degenerate moderately-coupled plasma. Our findings agree well with recent quantum molecular dynamics simulations.

We present a new computational approach to simulate linear sweep and cyclic voltammetry experiments that does not require a discretized grid in space to quantify diffusion. By using a Green's function solution coupled to a standard implicit ordinary differential equation solver, we are able to simulate current and redox species concentrations using only a small grid in time. As a result, where benchmarking is possible, we find that the current method is faster (and quantitatively identical) to established techniques. The present algorithm should help open the door to studying adsorption effects in inner sphere electrochemistry.

Bunch splitting is an RF manipulation method of changing the bunch structure, bunch numbers and bunch intensity in the high-intensity synchrotrons that serve as the injector for a particle collider. An efficient way to realize bunch splitting is to use the combination of different harmonic RF systems, such as the two-fold bunch splitting of a bunch with a combination of fundamental harmonic and doubled harmonic RF systems. The two-fold bunch splitting and three-fold bunch splitting methods have been experimentally verified and successfully applied to the LHC/PS. In this paper, a generalized multi-fold bunch splitting method is given. The five-fold bunch splitting method using specially designed multi-harmonic RF systems was studied and tentatively applied to the medium-stage synchrotron (MSS), the third accelerator of the injector chain of the Super Proton-Proton Collider (SPPC), to mitigate the pileup effects and collective instabilities of a single bunch in the SPPC. The results show that the five-fold bunch splitting is feasible and both the bunch population distribution and longitudinal emittance growth after the splitting are acceptable, e.g., a few percent in the population deviation and less than 10% in the total emittance growth.

We present a numerical study of the resonant high harmonic generation by tin ions in an elliptically-polarised laser field along with a simple analytical model revealing the mechanism and main features of this process. We show that the yield of the resonant harmonics behaves anomalously with the fundamental field ellipticity, namely the drop of the resonant harmonic intensity with the fundamental ellipticity is much slower than for high harmonics generated through the nonresonant mechanism. Moreover, we study the polarisation properties of high harmonics generated in elliptically-polarised field and show that the ellipticity of harmonics near the resonance is significantly higher than for ones far off the resonance. This introduces a prospective way to create a source of the quasi-monochromatic coherent XUV with controllable ellipticity potentially up to circular.

Parker Solar Probe (PSP) is providing an unprecedented view of the Sun's corona as it progressively dips closer into the solar atmosphere with each solar encounter. Each set of observations provides a unique opportunity to test and constrain global models of the solar corona and inner heliosphere and, in turn, use the model results to provide a global context for interpreting such observations. In this study, we develop a set of global magnetohydrodynamic (MHD) model solutions of varying degrees of sophistication for PSP's first four encounters and compare the results with in situ measurements from PSP, Stereo-A, and Earth-based spacecraft, with the objective of assessing which models perform better or worse. All models were primarily driven by the observed photospheric magnetic field using data from Solar Dynamics Observatory's Helioseismic and Magnetic Imager (HMI) instrument. Overall, we find that there are substantial differences between the model results, both in terms of the large-scale structure of the inner heliosphere during these time periods, as well as in the inferred time-series at various spacecraft. The "thermodynamic" model, which represents the "middle ground", in terms of model complexity, appears to reproduce the observations most closely for all four encounters. Our results also contradict an earlier study that had hinted that the open flux problem may disappear nearer the Sun. Instead, our results suggest that this "missing" solar flux is still missing even at 26.9 Rs, and thus it cannot be explained by interplanetary processes. Finally, the model results were also used to provide a global context for interpreting the localized in situ measurements.

Methods for processing point cloud information have seen a great success in collider physics applications. One recent breakthrough in machine learning is the usage of Transformer networks to learn semantic relationships between sequences in language processing. In this work, we apply a modified Transformer network called Point Cloud Transformer as a method to incorporate the advantages of the Transformer architecture to an unordered set of particles resulting from collision events. To compare the performance with other strategies, we study jet-tagging applications for highly-boosted particles.

Ion implantation underpins a vast range of devices and technologies that require precise control over the physical, chemical, electronic, magnetic and optical properties of materials. A variant termed recoil implantation - in which a precursor is deposited onto a substrate as a thin film and implanted via momentum transfer from incident energetic ions - has a number of compelling advantages, particularly when performed using an inert ion nano-beam [Fröch et al., Nat Commun 11, 5039 (2020)]. However, a major drawback of this approach is that the implant species are limited to the constituents of solid thin films. Here we overcome this limitation by demonstrating recoil implantation using gas-phase precursors. Specifically, we fabricate nitrogen-vacancy (NV) color centers in diamond using an Ar ion beam and the nitrogen-containing precursor gases N2, NH3 and NF3. Our work expands the applicability of recoil implantation to most of the periodic table, and to applications in which thin film deposition or removal is impractical.