Scalar Dark Matter and Standard Model with Four Generations

We consider a scalar dark matter model, the SM4+D, consisting of the standard model with four generations (SM4) and a real gauge-singlet scalar called darkon, D, as the weakly interacting massive particle (WIMP) dark-matter (DM) candidate. We explore constraints on the darkon sector of the SM4+D from WIMP DM direct-search experiments, including CDMS II and CoGeNT, and from the decay of a B meson into a kaon plus missing energy. We find that a sizable portion of the darkon parameter space is still compatible with the experimental data. Since the darkon-Higgs interaction may give rise to considerable enhancement of the Higgs invisible decay mode, the existence of the darkon could lead to the weakening or evasion of some of the restrictions on the Higgs mass in the presence of fourth-generation quarks. In addition, it can affect the flavor-changing decays of these new heavy quarks into a lighter quark and the Higgs boson, as the Higgs may subsequently decay invisibly. Therefore we also study these flavor-changing neutral transitions involving the darkon, as well as the corresponding top-quark decay t -> cDD, some of which may be observable at the Tevatron or LHC and thus provide additional tests for the SM4+D.Comment: 18 pages, 5 figure

Constraints on Unparticle Interactions from Invisible Decays of Z, Quarkonia and Neutrinos

Unparticles (\U) interact weakly with particles. The direct signature of unparticles will be in the form of missing energy. We study constraints on unparticle interactions using totally invisible decay modes of $Z$, vector quarkonia $V$ and neutrinos. The constraints on the unparticle interaction scale \Lambda_\U are very sensitive to the dimension d_\U of the unparticles. From invisible $Z$ and $V$ decays, we find that with d_\U close to 1 for vector \U, the unparticle scale \Lambda_\U can be more than $10^4$ TeV, and for d_\U around 2, the scale can be lower than one TeV. From invisible neutrino decays, we find that if d_\U is close to 3/2, the scale can be more than the Planck mass, but with d_\U around 2 the scale can be as low as a few hundred GeV. We also study the possibility of using V (Z)\to \gamma + \U to constrain unparticle interactions, and find that present data give weak constraints.Comment: 12 pages, 4 figures, version to appear in JHEP

Pomeranchuk effect and spin-gradient cooling of Bose-Bose mixtures in an optical lattice

We theoretically investigate finite-temperature thermodynamics and demagnetization cooling of two-component Bose-Bose mixtures in a cubic optical lattice, by using bosonic dynamical mean field theory (BDMFT). We calculate the finite-temperature phase diagram, and remarkably find that the system can be heated from the superfluid into the Mott insulator at low temperature, analogous to the Pomeranchuk effect in 3He. This provides a promising many-body cooling technique. We examine the entropy distribution in the trapped system and discuss its dependence on temperature and an applied magnetic field gradient. Our numerical simulations quantitatively validate the spin-gradient demagnetization cooling scheme proposed in recent experiments.Comment: 9 pages, 8 figure

Learning to Predict the Cosmological Structure Formation

Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe.Comment: 8 pages, 5 figures, 1 tabl

Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization

In this paper, we consider the alignment between an upstream dimensionality reduction task of learning a low-dimensional representation of a set of high-dimensional data and a downstream optimization task of solving a stochastic program parameterized by said representation. In this case, standard dimensionality reduction methods (e.g., principal component analysis) may not perform well, as they aim to maximize the amount of information retained in the representation and do not generally reflect the importance of such information in the downstream optimization problem. To address this problem, we develop a prescriptive dimensionality reduction framework that aims to minimize the degree of suboptimality in the optimization phase. For the case where the downstream stochastic optimization problem has an expected value objective, we show that prescriptive dimensionality reduction can be performed via solving a distributionally-robust optimization problem, which admits a semidefinite programming relaxation. Computational experiments based on a warehouse transshipment problem and a vehicle repositioning problem show that our approach significantly outperforms principal component analysis with real and synthetic data sets

Detecting Galaxy-Filament Alignments in the Sloan Digital Sky Survey III

Previous studies have shown the filamentary structures in the cosmic web influence the alignments of nearby galaxies. We study this effect in the LOWZ sample of the Sloan Digital Sky Survey using the "Cosmic Web Reconstruction" filament catalogue. We find that LOWZ galaxies exhibit a small but statistically significant alignment in the direction parallel to the orientation of nearby filaments. This effect is detectable even in the absence of nearby galaxy clusters, which suggests it is an effect from the matter distribution in the filament. A nonparametric regression model suggests that the alignment effect with filaments extends over separations of 30-40 Mpc. We find that galaxies that are bright and early-forming align more strongly with the directions of nearby filaments than those that are faint and late-forming; however, trends with stellar mass are less statistically significant, within the narrow range of stellar mass of this sample.Comment: 14 pages, 13 figures. Accepted to the MNRA

The Trapping and Characterization of a Single Hydrogen Molecule in a Continuously Tunable Nanocavity

Using inelastic electron tunneling spectroscopy with the scanning tunneling microscope (STM-IETS) and density functional theory calculations (DFT), we investigated properties of a single H2 molecule trapped in nanocavities with controlled shape and separation between the STM tip and the Au (110) surface. The STM tip not only serves for the purpose of characterization, but also is directly involved in modification of chemical environment of molecule. The bond length of H2 expands in the atop cavity, with a tendency of dissociation when the gap closes, whereas it remains unchanged in the trough cavity. The availability of two substantially different cavities in the same setup allows understanding of H2 adsorption on noble metal surfaces and sets a path for manipulating a single chemical bond by design.Comment: 11 pages, 4 figure