An effective Hamiltonian for phase fluctuations on a lattice: an extended XY model

We derive an effective Hamiltonian for phase fluctuations in an s-wave superconductor starting from the attractive Hubbard model on a square lattice. In contrast to the common assumption, we find that the effective Hamiltonian is not the usual XY model but is of an extended XY type. This extended feature is robust and leads to essential corrections in understanding phase fluctuations on a lattice. The effective coupling in the Hamiltonian varies significantly with temperature.Comment: 2 figure

Hamilton's Turns for the Lorentz Group

Hamilton in the course of his studies on quaternions came up with an elegant geometric picture for the group SU(2). In this picture the group elements are represented by ``turns'', which are equivalence classes of directed great circle arcs on the unit sphere $S^2$, in such a manner that the rule for composition of group elements takes the form of the familiar parallelogram law for the Euclidean translation group. It is only recently that this construction has been generalized to the simplest noncompact group $SU(1,1) = Sp(2, R) = SL(2,R)$, the double cover of SO(2,1). The present work develops a theory of turns for $SL(2,C)$, the double and universal cover of SO(3,1) and $SO(3,C)$, rendering a geometric representation in the spirit of Hamilton available for all low dimensional semisimple Lie groups of interest in physics. The geometric construction is illustrated through application to polar decomposition, and to the composition of Lorentz boosts and the resulting Wigner or Thomas rotation.Comment: 13 pages, Late

Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers

To control a dynamical system it is essential to obtain an accurate estimate of the current system state based on uncertain sensor measurements and existing system knowledge. An optimization-based moving horizon estimation (MHE) approach uses a dynamical model of the system, and further allows for integration of physical constraints on system states and uncertainties, to obtain a trajectory of state estimates. In this work, we address the problem of state estimation in the case of constrained linear systems with parametric uncertainty. The proposed approach makes use of differentiable convex optimization layers to formulate an MHE state estimator for systems with uncertain parameters. This formulation allows us to obtain the gradient of a squared and regularized output error, based on sensor measurements and state estimates, with respect to the current belief of the unknown system parameters. The parameters within the MHE problem can then be updated online using stochastic gradient descent (SGD) to improve the performance of the MHE. In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.Comment: This paper was accepted for presentation at the 4th Annual Conference on Learning for Dynamics and Control. The extended version here contains an additional appendix with more details on the numerical exampl

Role of many-body entanglement in decoherence processes

A pure state decoheres into a mixed state as it entangles with an environment. When an entangled two-mode system is embedded in a thermal environment, however, each mode may not be entangled with its environment by their simple linear interaction. We consider an exactly solvable model to study the dynamics of a total system, which is composed of an entangled two-mode system and a thermal environment, and also an array of infinite beam splitters. It is shown that many-body entanglement of the system and the environment plays a crucial role in the process of disentangling the system.Comment: 4 pages, 1 figur

Quantum simulation of frustrated magnetism in triangular optical lattices

Magnetism plays a key role in modern technology as essential building block of many devices used in daily life. Rich future prospects connected to spintronics, next generation storage devices or superconductivity make it a highly dynamical field of research. Despite those ongoing efforts, the many-body dynamics of complex magnetism is far from being well understood on a fundamental level. Especially the study of geometrically frustrated configurations is challenging both theoretically and experimentally. Here we present the first realization of a large scale quantum simulator for magnetism including frustration. We use the motional degrees of freedom of atoms to comprehensively simulate a magnetic system in a triangular lattice. Via a specific modulation of the optical lattice, we can tune the couplings in different directions independently, even from ferromagnetic to antiferromagnetic. A major advantage of our approach is that standard Bose-Einstein-condensate temperatures are sufficient to observe magnetic phenomena like N\'eel order and spin frustration. We are able to study a very rich phase diagram and even to observe spontaneous symmetry breaking caused by frustration. In addition, the quantum states realized in our spin simulator are yet unobserved superfluid phases with non-trivial long-range order and staggered circulating plaquette currents, which break time reversal symmetry. These findings open the route towards highly debated phases like spin-liquids and the study of the dynamics of quantum phase transitions.Comment: 5 pages, 4 figure

Composite Fermions with Orbital Magnetization

For quantum Hall systems, in the limit of large magnetic field (or equivalently small electron band mass $m_b$), the static response of electrons to a spatially varying magnetic field is largely determined by kinetic energy considerations. This response is not correctly given in existing approximations based on the Fermion Chern-Simons theory of the partially filled Landau level. We remedy this problem by attaching an orbital magnetization to each fermion to separate the current into magnetization and transport contributions, associated with the cyclotron and guiding center motions respectively. This leads to a Chern-Simons Fermi liquid description of the $\nu=\frac{1}{2m}$ state which correctly predicts the $m_b$ dependence of the static and dynamic response in the limit $m_b \rightarrow 0$.Comment: 4 pages, RevTeX, no figure

The Forestecology R Package for Fitting and Assessing Neighborhood Models of the Effect of Interspecific Competition on the Growth of Trees

Neighborhood competition models are powerful tools to measure the effect of interspecific competition. Statistical methods to ease the application of these models are currently lacking. We present the forestecology package providing methods to (a) specify neighborhood competition models, (b) evaluate the effect of competitor species identity using permutation tests, and (cs) measure model performance using spatial cross-validation. Following Allen and Kim (PLoS One, 15, 2020, e0229930), we implement a Bayesian linear regression neighborhood competition model. We demonstrate the package\u27s functionality using data from the Smithsonian Conservation Biology Institute\u27s large forest dynamics plot, part of the ForestGEO global network of research sites. Given ForestGEO’s data collection protocols and data formatting standards, the package was designed with cross-site compatibility in mind. We highlight the importance of spatial cross-validation when interpreting model results. The package features (a) tidyverse-like structure whereby verb-named functions can be modularly “piped” in sequence, (b) functions with standardized inputs/outputs of simple features sf package class, and (c) an S3 object-oriented implementation of the Bayesian linear regression model. These three facts allow for clear articulation of all the steps in the sequence of analysis and easy wrangling and visualization of the geospatial data. Furthermore, while the package only has Bayesian linear regression implemented, the package was designed with extensibility to other methods in mind

Correlations in Networks associated to Preferential Growth

Combinations of random and preferential growth for both on-growing and stationary networks are studied and a hierarchical topology is observed. Thus for real world scale-free networks which do not exhibit hierarchical features preferential growth is probably not the main ingredient in the growth process. An example of such real world networks includes the protein-protein interaction network in yeast, which exhibits pronounced anti-hierarchical features.Comment: 4 pages, 4 figure