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Miniaturized multidimensional peptide separations can greatly increase the coverage in proteome profiling for trace samples.
Classical novae are thermonuclear explosions that take place in the envelopes of accreting white dwarfs in binary systems. The material piles up under degenerate conditions, driving a thermonuclear runaway. The energy released by the suite of nuclear processes operating at the envelope heats the material up to peak temperatures of ~(1-4) × 10 8 K. During these events, about 10 -3 -10 -7 M ⊙ , enriched in CNO and, sometimes, other intermediate-mass elements (e.g., Ne, Na, Mg, Al) are ejected into the interstellar medium. To account for the gross observational properties of classical novae (in particular, the high concentrations of metals spectroscopically inferred in the ejecta), models require mixing between the (solar-like) material transferred from the secondary and the outermost layers (CO- or ONe-rich) of the underlying white dwarf. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. However, the feasibility of this mechanism has been explored in the framework of CO white dwarfs, while mixing with different substrates still needs to be properly addressed. We performed three-dimensional simulations of mixing at the core-envelope interface during nova outbursts with the multidimensional code FLASH, for two types of substrates: CO- and ONe-rich. We also show that the presence of an ONe-rich substrate, as in “neon novae”, yields higher metallicity enhancements in the ejecta than CO-rich substrates (i.e., non-neon novae). Finally, a number of requirements and constraints for such 3D simulations (e.g., minimum resolution, size of the computational domain) are also outlined.
Context. Classical novae are explosive phenomena that take place in stellar binary systems. They are powered by mass transfer from a low-mass main sequence star onto either a CO or ONe white dwarf. The material accumulates for 104–105 yr until ignition under degenerate conditions, resulting in a thermonuclear runaway. The nuclear energy released produces peak temperatures of ~0.1–0.4 GK. During these events, 10 –7 –10 –3 M ⊙ enriched in intermediate-mass elements, with respect to solar abundances, are ejected into the interstellar medium. Yet, the origin of the large metallicity enhancements and the inhomogeneous distribution of chemical species observed in high-resolution spectra of ejected nova shells is not fully understood. Aims. Recent multidimensional simulations have demonstrated that Kelvin-Helmholtz instabilities that operate at the core-envelope interface can naturally produce self-enrichment of the accreted envelope with material from the underlying white dwarf at levels that agree with observations. Yet, such multidimensional simulations have been performed for a small number of cases and much of the parameter space remains unexplored. Methods. We explored the dredge-up, driven by Kelvin-Helmholtz instabilities, for white dwarf masses in the range 0.8–1.25 M ⊙ and different core compositions, that is, CO-rich and ONe-rich substrates. We present a set of five numerical simulations performed in two dimensions aimed at analyzing the possible impact of the white dwarf mass, and composition, on the metallicity enhancement and explosion characteristics. Results. At the time we stop the simulations, we observe greater mixing (~30% higher when measured in the same conditions) and more energetic outbursts for ONe-rich substrates than for CO-rich substrates and more massive white dwarfs.
Zero-point and excited level splittings due to double-proton tunneling are calculated for porphycene and the results are compared with experiment. The calculation makes use of a multidimensional imaginary-mode Hamiltonian, diagonalized directly by an effective reduction of its dimensionality. Porphycene has a complex potential energy surface with nine stationary configurations that allow a variety of tunneling paths, many of which include classically accessible regions. A symmetry-based approach is used to show that the zero-point level, although located above the cis minimum, corresponds to concerted tunneling along a direct trans − trans path; a corresponding cis − cis path is predicted at higher energy. This supports the conclusion of a previous paper [Z. Smedarchina, W. Siebrand, and A. Fernández-Ramos, J. Chem. Phys. 127, 174513 (2007)] based on the instanton approach to a model Hamiltonian of correlated double-proton transfer. A multidimensional tunneling Hamiltonian is then generated, based on a double-minimum potential along the coordinate of concerted proton motion, which is newly evaluated at the RI-CC2/cc-pVTZ level of theory. To make it suitable for diagonalization, its dimensionality is reduced by treating fast weakly coupled modes in the adiabatic approximation. This results in a coordinate-dependent mass of tunneling, which is included in a unique Hermitian form into the kinetic energy operator. The reduced Hamiltonian contains three symmetric and one antisymmetric mode coupled to the tunneling mode and is diagonalized by a modified Jacobi-Davidson algorithm implemented in the Jadamilu software for sparse matrices. The results are in satisfactory agreement with the observed splitting of the zero-point level and several vibrational fundamentals after a partial reassignment, imposed by recently derived selection rules. They also agree well with instanton calculations based on the same Hamiltonian.
Density-functional theory simulations of CZTS, CZTSe, and CZTS{sub 0.25}Se{sub 0.75} photovoltaic compounds have been performed to investigate the stability of the CZTS{sub 0.25}Se{sub 0.75} alloy vs. decomposition into CZTS, CZTSe, and other secondary compounds. The Gibbs energy for vibrational contributions was estimated by calculating phonon spectra and thermodynamic properties at finite temperatures. It was demonstrated that the CZTS{sub 0.25}Se{sub 0.75} alloy is stabilized not by enthalpy of formation but primarily by the mixing contributions to the Gibbs energy. The Gibbs energy gains/losses for several decomposition reactions were calculated as a function of temperature with/without intermixing and vibration contributions to the Gibbs energy. A set of phase diagrams was built in the multidimensional space of chemical potentials at 300 K and 900 K temperatures to demonstrate alloy stability and boundary compounds at various chemical conditions. It demonstrated for CZTS{sub 0.25}Se{sub 0.75} that the chemical potentials for stability differ between typical processing temperature (∼900 K) and operating temperature (300 K). This implies that as cooling progresses, the flux/concentration of S should be increased in MBE growth to maintain the CZTS{sub 0.25}Se{sub 0.75} in a thermodynamically stable state to minimize phase decomposition.
The mobility of defects such as dislocations controls the mechanical properties of metals. This mobility is determined both by the characteristics of the defect and the material, as well as the local stress and strain applied to the defect. Therefore, the knowledge of the stress and strain during deformation at the scale of defects is important for understanding fundamental deformation mechanisms. In this paper, we demonstrate a method of measuring local stresses and strains during continuous in situ deformation with a resolution of a few nanometers using nanodiffraction strain mapping. Finally, our results demonstrate how large multidimensional data sets captured with high speed electron detectors can be analyzed in multiple ways after an in situ TEM experiment, opening the door for true multimodal analysis from a single electron scattering experiment.
In this work, imaging mechanisms in contact Kelvin probe force microscopy (cKPFM) are explored via information theory-based methods. Gaussian processes are used to achieve super-resolution in the cKPFM signal, effectively extrapolating across the spatial and parameter space. Tensor factorization is applied to reduce the multidimensional signal to the tensor convolution of the scalar functions that show a clear trending behavior with the imaging parameters. These methods establish a workflow for the analysis of the multidimensional datasets that can then be related to the relevant physical mechanisms. We also provide an interactive Google Colab notebook that goes through all the analyses discussed in the paper.
Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.
Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).
Considerable progress has been made in deuterium-tritium-layered implosion experiments on the OMEGA Laser System, bringing the prospects for thermonuclear ignition in direct-drive configurations with megajoule-class lasers closer to reality. Doing so has required navigating the balance between improved 1D performance and multidimensional stability. Using statistical modeling based on over 350 cryogenic implosions to identify various degradation mechanisms, and combined with multidimensional simulations and experimental techniques such as target offsets to combat residual flows, core conditions have repeatably been achieved that extrapolate to the burning-plasma state when scaled to 2.15 MJ of symmetric laser illumination. Using high implosion velocities (> 450 km/s) and moderately high adiabats (~5), these experiments produced record-high scaled Lawson parameters in direct drive equal to 89 ± 2% of that required for ignition with expected yields of up to 1.5 ± 0.2 MJ. To improve these results still further, focused physics studies are performed to improve physics understanding and identify routes to even greater performance. Recent studies include investigations into the impact of mounting features, laser imprint, reduced fuel temperatures, and greater on-target intensities through subscale experiments. This manuscript gives a summary of the cryogenic direct-drive program on the OMEGA laser, including routes taken to achieve the current best performance, the status of recent focused physics investigations, and future designs—such as target solutions to laser imprint and reducing vapor density to increase convergence—that are expected lead to the demonstration of hydro-equivalent ignition on OMEGA.
Merging carbon-oxygen (CO) white dwarfs are a promising progenitor system for Type Ia supernovae (SNe Ia), but the underlying physics and timing of the detonation are still debated. If an explosion occurs after the secondary star is fully disrupted, the exploding primary will expand into a dense CO medium that may still have a disk-like structure. This interaction will decelerate and distort the ejecta. Here we carry out multidimensional simulations of 'tamped' SN Ia models, using both particle and grid-based codes to study the merger and explosion dynamics and a radiative transfer code to calculate synthetic spectra and light curves. We find that post-merger explosions exhibit an hourglass-shaped asymmetry, leading to strong variations in the light curves with viewing angle. The two most important factors affecting the outcome are the scale height of the disk, which depends sensitively on the binary mass ratio, and the total {sup 56}Ni yield, which is governed by the central density of the remnant core. The synthetic broadband light curves rise and decline very slowly, and the spectra generally look peculiar, with weak features from intermediate mass elements but relatively strong carbon absorption. We also consider the effects of the viscous evolution of the remnant and show that a longer time delay between merger and explosion probably leads to larger {sup 56}Ni yields and more symmetrical remnants. We discuss the relevance of this class of aspherical 'tamped' SN Ia for explaining the class of 'super-Chandrasekhar' SN Ia.
We apply the mathematical formalism of vector spherical harmonics decomposition to convective stellar velocity fields from multidimensional hydrodynamics simulations and show that the resulting power spectra furnish a robust and stable statistical description of stellar convective turbulence. Analysis of the power spectra helps identify key physical parameters of the convective process such as the dominant scale of the turbulent motions that influence the structure of massive evolved pre-supernova stars. We introduce the numerical method that can be used to calculate vector spherical harmonics power spectra from two-dimensional (2D) and three-dimensional (3D) convective shell simulation data. Using this method we study the properties of oxygen shell burning and convection for a 15 M {sub ☉} star simulated by the hydrodynamics code FLASH in 2D and 3D. We discuss the importance of realistic initial conditions to achieving successful core-collapse supernova explosions in multidimensional simulations. We show that the calculated power spectra can be used to generate realizations of the velocity fields of presupernova convective shells. We find that the slope of the solenoidal mode power spectrum remains mostly constant throughout the evolution of convection in the oxygen shell in both 2D and 3D simulations. We also find that the characteristic radial scales of the convective elements are smaller in 3D than in 2D, while the angular scales are larger in 3D.
We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs). Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost. We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard Lambda Cold Dark Matter N-body simulation. The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation. We test the ability of the PSO algorithm to find the best set of free parameters of the model by comparing the results with those obtained using a MCMC exploration. Both methods find the same maximum likelihood region, however, the PSO method requires one order of magnitude fewer evaluations. This new approach allows a fast estimation of the best-fitting parameter set in multidimensional spaces, providing a practical tool to test the consequences of including other astrophysical processes in SAMs.
In this paper, we present a novel methodology based on machine learning to extract lattice variations in crystalline materials, at the nanoscale, from an x-ray Bragg diffraction-based imaging technique. By employing a full-field microscopy setup, we capture real space images of materials, with imaging contrast determined solely by the x-ray diffracted signal. The data sets that emanate from this imaging technique are a hybrid of real space information (image spatial support) and reciprocal lattice space information (image contrast), and are intrinsically multidimensional (5D). By a judicious application of established unsupervised machine learning techniques and multivariate analysis to this multidimensional data cube, we show how to extract features that can be ascribed physical interpretations in terms of common structural distortions, such as lattice tilts and dislocation arrays. Finally, we demonstrate this 'big data' approach to x-ray diffraction microscopy by identifying structural defects present in an epitaxial ferroelectric thin-film of lead zirconate titanate.
For different gravitational models we consider limitations on their parameters coming from recent observational data for type Ia supernovae, baryon acoustic oscillations, and from 34 data points for the Hubble parameter H(z) depending on redshift. We calculate parameters of 3 models describing accelerated expansion of the universe: the ΛCDM model, the model with generalized Chaplygin gas (GCG) and the multidimensional model of I. Pahwa, D. Choudhury and T.R. Seshadri. In particular, for the ΛCDM model 1σ estimates of parameters are: H{sub 0}=70.262±0.319 km {sup -1}Mp {sup -1}, Ω{sub m}=0.276{sub -0.008}{sup +0.009}, Ω{sub Λ}=0.769±0.029, Ω{sub k}=-0.045±0.032. The GCG model under restriction 0α≥ is reduced to the ΛCDM model. Predictions of the multidimensional model essentially depend on 3 data points for H(z) with z≥2.3.
We compare the general effective theory of one-body dark matter nucleon interactions to current direct detection experiments in a global multidimensional statistical analysis. We derive exclusion limits on the 28 isoscalar and isovector coupling constants of the theory, and show that current data place interesting constraints on dark matter-nucleon interaction operators usually neglected in this context. We characterize the interference patterns that can arise in dark matter direct detection from pairs of dark matter-nucleon interaction operators, or from isoscalar and isovector components of the same operator. We find that commonly neglected destructive interference effects weaken standard direct detection exclusion limits by up to one order of magnitude in the coupling constants.
In this study, we present results from a study of the photometric redshift performance of the Dark Energy Survey (DES), using the early data from a Science Verification period of observations in late 2012 and early 2013 that provided science-quality images for almost 200 sq. deg. at the nominal depth of the survey. We assess the photometric redshift (photo-z) performance using about 15 000 galaxies with spectroscopic redshifts available from other surveys. These galaxies are used, in different configurations, as a calibration sample, and photo-z's are obtained and studied using most of the existing photo-z codes. A weighting method in a multidimensional colour–magnitude space is applied to the spectroscopic sample in order to evaluate the photo-z performance with sets that mimic the full DES photometric sample, which is on average significantly deeper than the calibration sample due to the limited depth of spectroscopic surveys. In addition, empirical photo-z methods using, for instance, artificial neural networks or random forests, yield the best performance in the tests, achieving core photo-z resolutions σ68 ~ 0.08. Moreover, the results from most of the codes, including template-fitting methods, comfortably meet the DES requirements on photo-z performance, therefore, providing an excellent precedent for future DES data sets.
Two-dimensional terahertz spectroscopy (2DTS), a terahertz analog of nuclear magnetic resonance, is a new technique poised to address many open questions in complex condensed matter systems. The conventional theoretical framework used ubiquitously for interpreting multidimensional spectra of discrete quantum level systems is, however, insufficient for the continua of collective excitations in strongly correlated materials. Here, in this work, we develop a theory for 2DTS of a model collective excitation, the Josephson plasma resonance in layered superconductors. Starting from a mean-field approach at temperatures well below the superconducting phase transition, we obtain expressions for the multidimensional nonlinear responses that are amenable to intuition derived from the conventional single-mode scenario. We then consider temperatures near the superconducting critical temperature T c , where dynamics beyond mean-field become important and conventional intuition fails. As fluctuations proliferate near T c , the dominant contribution to nonlinear response comes from an optical parametric drive of counterpropagating Josephson plasmons, which gives rise to 2D spectra that are qualitatively different from the mean-field predictions. As such, and in contrast to one-dimensional spectroscopy techniques, such as third harmonic generation, 2DTS can be used to directly probe thermally excited finite-momentum plasmons and their interactions. Our theory can readily be tested in cuprates, and we discuss implications beyond the present context of Josephson plasmons.