Publications

Layered perovskite ruthenium oxides exhibit a striking series of metal-insulator and magnetic-nonmagnetic phase transitions easily tuned by temperature, pressure, epitaxy, and nonlinear drive. In this work, we combine results from two complementary state of the art many-body methods, Auxiliary Field Quantum Monte Carlo and Dynamical Mean Field Theory, to determine the low-temperature phase diagram of Ca2RuO4. Both methods predict a low temperature, pressure-driven metal-insulator transition accompanied by a ferromagnetic-antiferromagnetic transition. The properties of the ferromagnetic state vary non-monotonically with pressure and are dominated by the ruthenium dxy orbital, while the properties of the antiferromagnetic state are dominated by the dxz and dyz orbitals. Differences of detail in the predictions of the two methods are analyzed. This work is theoretically important as it presents the first application of the Auxiliary Field Quantum Monte Carlo method to an orbitally-degenerate system with both Mott and Hunds physics, and provides an important comparison of the Dynamical Mean Field and Auxiliary Field Quantum Monte Carlo methods.

We present a new integral representation for the unsteady, incompressible Stokes or Navier-Stokes equations, based on a linear combination of heat and harmonic potentials. For velocity boundary conditions, this leads to a coupled system of integral equations: one for the normal component of velocity and one for the tangential components. Each individual equation is well-condtioned, and we show that using them in predictor-corrector fashion, combined with spectral deferred correction, leads to high-order accuracy solvers. The fundamental unknowns in the mixed potential representation are densities supported on the boundary of the domain. We refer to one as the vortex source, the other as the pressure source and the coupled system as the combined source integral equation.

Supersonic turbulence results in strong density fluctuations in the interstellar medium (ISM), which have a profound effect on the chemical structure. Particularly useful probes of the diffuse ISM are the ArH+, OH+, H2O+ molecular ions, which are highly sensitive to fluctuations in the density and the H2 abundance. We use isothermal magnetohydrodynamic (MHD) simulations of various sonic Mach numbers, s, and density decorrelation scales, ydec, to model the turbulent density field. We post-process the simulations with chemical models and obtain the probability density functions (PDFs) for the H2, ArH+, OH+ and H2O+ abundances. We find that the PDF dispersions increases with increasing s and ydec, as the magnitude of the density fluctuations increases, and as they become more coherent. Turbulence also affects the median abundances: when s and ydec are high, low-density regions with low H2 abundance become prevalent, resulting in an enhancement of ArH+ compared to OH+ and H2O+. We compare our models with Herschel observations. The large scatter in the observed abundances, as well as the high observed ArH+/OH+ and ArH+/H2O+ ratios are naturally reproduced by our supersonic (s=4.5), large decorrelation scale (ydec=0.8) model, supporting a scenario of a large-scale turbulence driving. The abundances also depend on the UV intensity, CR ionization rate, and the cloud column density, and the observed scatter may be influenced by fluctuations in these parameters.

Accurate and predictive computations of the quantum-mechanical behavior of many interacting electrons in realistic atomic environments are critical for the theoretical design of materials with desired properties. Such computations require the solution of the grand-challenge problem of the many-electron Schrodinger equation. An infinite chain of equispaced hydrogen atoms is perhaps the simplest realistic model for a bulk material, embodying several central characters of modern condensed matter physics and chemistry, while retaining a connection to the paradigmatic Hubbard model. Here we report the combined application of different cutting-edge computational methods to determine the properties of the hydrogen chain in its quantum-mechanical ground state. Varying the separation between the nuclei mimics applying pressure to a crystal, which we find leads to a rich phase diagram, including an antiferromagnetic Mott phase, electron density dimerization with power-law correlations, an insulator-to-metal transition and an intricate set of intertwined magnetic orders. Our work highlights the importance of the hydrogen chain as a model system for correlated materials, and introduces methodologies for more general studies of the quantum many-body problem in solids.

We have constructed a new timescale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar timescale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected timescale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based timescales are unlikely to contribute to the stability of the best timescales over the next decade, but they will remain a valuable independent check on atomic timescales. We also find that the stability of the pulsar-based timescale is likely to be limited by our knowledge of solar-system dynamics, and that errors in TT(BIPM17) will not be a limiting factor for the primary goal of the IPTA, which is to search for the signatures of nano-Hertz gravitational waves.

Cosmological studies of large-scale structure have relied on two-point statistics, not fully exploiting the rich structure of the cosmic web. In this paper we show how to capture some of this cosmic web information by using the minimum spanning tree (MST), for the first time using it to estimate cosmological parameters in simulations. Discrete tracers of dark matter such as galaxies, N-body particles or haloes are used as nodes to construct a unique graph, the MST, that traces skeletal structure. We study the dependence of the MST on cosmological parameters using haloes from a suite of COLA simulations with a box size of 250 h−1Mpc, varying the amplitude of scalar fluctuations (As), matter density (Ωm), and neutrino mass (∑mν). The power spectrum P and bispectrum B are measured for wavenumbers between 0.125 and 0.5 hMpc−1, while a corresponding lower cut of ∼12.6 h−1Mpc is applied to the MST. The constraints from the individual methods are fairly similar but when combined we see improved 1σ constraints of ∼17% (∼12%) on Ωm and ∼12% (∼10%) on As with respect to P (P+B) thus showing the MST is providing additional information. The MST can be applied to current and future spectroscopic surveys (BOSS, DESI, Euclid, PSF, WFIRST, and 4MOST) in 3D and photometric surveys (DES and LSST) in tomographic shells to constrain parameters and/or test systematics.

Fish schools and bird flocks exhibit complex collective dynamics whose self-organization principles are largely unknown. The influence of hydrodynamics on such collectives has been relatively unexplored theoretically, in part due to the difficulty in modeling the temporally long-lived hydrodynamic interactions between many dynamic bodies. We address this through a novel discrete-time dynamical system (iterated map) that describes the hydrodynamic interactions between flapping swimmers arranged in one- and two-dimensional lattice formations. Our 1D results exhibit good agreement with previously published experimental data, in particular predicting the bistability of schooling states and new instabilities that can be probed in experimental settings. For 2D lattices, we determine the formations for which swimmers optimally benefit from hydrodynamic interactions. We thus obtain the following hierarchy: while a side-by-side single-row “phalanx” formation offers a small improvement over a solitary swimmer, 1D in-line and 2D rectangular lattice formations exhibit substantial improvements, with the 2D diamond lattice offering the largest hydrodynamic benefit. Generally, our self-consistent modeling framework may be broadly applicable to active systems in which the collective dynamics is primarily driven by a fluid-mediated memory.

Inventors of the original artificial neural networks (ANNs) derived their inspiration from biology [1]. However, today, most ANNs, such as backpropagation-based convolutional deeplearning networks, resemble natural NNs only superficially. Given that, on some tasks, such ANNs achieve human or even superhuman performance, why should one care about such dissimilarity with natural NNs? The algorithms of natural NNs are relevant if one's goal is not just to outperform humans on certain tasks but to develop general-purpose artificial intelligence rivaling that of a human. As contemporary ANNs are far from achieving this goal and natural NNs, by definition, achieve it, natural NNs must contain some "secret sauce" that ANNs lack. This is why we need to understand the algorithms implemented by natural NNs.

The nonequilibrium dynamics of correlated many-particle systems is of interest in connection with pump-probe experiments on molecular systems and solids, as well as theoretical investigations of transport properties and relaxation processes. Nonequilibrium Green's functions are a powerful tool to study interaction effects in quantum many-particle systems out of equilibrium, and to extract physically relevant information for the interpretation of experiments. We present the open-source software package NESSi (The Non-Equilibrium Systems Simulation package) which allows to perform many-body dynamics simulations based on Green's functions on the L-shaped Kadanoff-Baym contour. NESSi contains the library libcntr which implements tools for basic operations on these nonequilibrium Green's functions, for constructing Feynman diagrams, and for the solution of integral and integro-differential equations involving contour Green's functions. The library employs a discretization of the Kadanoff-Baym contour into time N points and a high-order implementation of integration routines. The total integrated error scales up to (N−7), which is important since the numerical effort increases at least cubically with the simulation time. A distributed-memory parallelization over reciprocal space allows large-scale simulations of lattice systems. We provide a collection of example programs ranging from dynamics in simple two-level systems to problems relevant in contemporary condensed matter physics, including Hubbard clusters and Hubbard or Holstein lattice models. The libcntr library is the basis of a follow-up software package for nonequilibrium dynamical mean-field theory calculations based on strong-coupling perturbative impurity solvers.

The Wide-Field Infrared Space Telescope (WFIRST) will be capable of delivering precise astrometry for faint sources over the enormous field of view of its main camera, the Wide-Field Imager (WFI). This unprecedented combination will be transformative for the many scientific questions that require precise positions, distances, and velocities of stars. We describe the expectations for the astrometric precision of the WFIRST WFI in different scenarios, illustrate how a broad range of science cases will see significant advances with such data, and identify aspects of WFIRST’s design where small adjustments could greatly improve its power as an astrometric instrument.