University of Arizona

About: University of Arizona is a education organization based out in Tucson, Arizona, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 63805 authors who have published 155998 publications receiving 6854915 citations. The organization is also known as: UA & U of A.

Astrometry and photometry for cool dwarfs and brown dwarfs

Abstract: Trigonometric parallax determinations are presented for 28 late-type dwarfs and brown dwarfs, including eight M dwarfs with spectral types between M7 and M9.5, 17 L dwarfs with spectral types between L0 and L8, and three T dwarfs. Broadband photometry at CCD wavelengths (VRIz*) and/or near-IR wavelengths (JHK) is presented for these objects and for 24 additional late-type dwarfs. Supplemented with astrometry and photometry from the literature, including 10 L and two T dwarfs with parallaxes established by association with bright, usually Hipparcos primaries, this material forms the basis for studying various color-color and color?absolute magnitude relations. The I-J color is a good predictor of absolute magnitude for late M and L dwarfs. MJ becomes monotonically fainter with I-J color and with spectral type through late L dwarfs, then brightens for early T dwarfs. The combination of z*JK colors alone can be used to classify late M, early L, and T dwarfs accurately, as well as to predict their absolute magnitudes, but is less effective at untangling the scatter among mid- and late L dwarfs. The mean tangential velocity of these objects is found to be slightly less than that for dM stars in the solar neighborhood, consistent with a sample with a mean age of several Gyr. Using colors to estimate bolometric corrections and models to estimate stellar radii, effective temperatures are derived. The latest L dwarfs are found to have Teff ~ 1360 K.

Are the Variations in Quasar Optical Flux Driven by Thermal Fluctuations

Abstract: We analyze a sample of optical light curves for 100 quasars, 70 of which have black hole mass estimates. Our sample is the largest and broadest used yet for modeling quasar variability. The sources in our sample have z < 2.8, 1042 λL λ(5100 A) 1046, and 106 M BH/M ☉ 1010. We model the light curves as a continuous time stochastic process, providing a natural means of estimating the characteristic timescale and amplitude of quasar variations. We employ a Bayesian approach to estimate the characteristic timescale and amplitude of flux variations; our approach is not affected by biases introduced from discrete sampling effects. We find that the characteristic timescales strongly correlate with black hole mass and luminosity, and are consistent with disk orbital or thermal timescales. In addition, the amplitude of short-timescale variations is significantly anticorrelated with black hole mass and luminosity. We interpret the optical flux fluctuations as resulting from thermal fluctuations that are driven by an underlying stochastic process, such as a turbulent magnetic field. In addition, the intranight variations in optical flux implied by our empirical model are 0.02 mag, consistent with current microvariability observations of radio-quiet quasars. Our stochastic model is therefore able to unify both long- and short-timescale optical variations in radio-quiet quasars as resulting from the same underlying process, while radio-loud quasars have an additional variability component that operates on timescales 1 day.

The Toda lattice. II. Existence of integrals

Abstract: Following recent computer studies which suggested that the equations of motion of Toda's exponential lattice should be completely H\'enon discovered analytical expressions for the constants of the motion. In the present paper, the existence of integrals is proved by a different method. Our approach shows the Toda lattice to be a finite-dimensional analog of the Korteweg-de Vries partial differential equation. Certain integrals of the Toda equations are the counterparts of the conserved quantities of the Korteweg-de Vries equation, and the theory initiated here has been used elsewhere to obtain solutions of the infinite lattice by inverse-scattering methods.

The Stellar Mass Assembly of Galaxies from z = 0 to z = 4: Analysis of a Sample Selected in the Rest-Frame Near-Infrared with Spitzer

Abstract: Using a sample of ~28,000 sources selected at 3.6-4.5 μm with Spitzer observations of the Hubble Deep Field North, the Chandra Deep Field South, and the Lockman Hole (surveyed area ~664 arcmin^2), we study the evolution of the stellar mass content of the universe at 0 10^12.0 M_☉) assembled the bulk of their stellar content rapidly (in 1-2 Gyr) beyond z ~ 3 in very intense star formation events (producing high specific SFRs). Galaxies with 10^11.5 2.5 is dominated by optically faint (Rgsim 25) red galaxies (distant red galaxies or BzK sources), which account for ~30% of the global population of galaxies, but contribute at least 60% of the cosmic stellar mass density. Bluer galaxies (e.g., Lyman break galaxies) are more numerous but less massive, contributing less than 50% of the global stellar mass density at high redshift.

Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter

Abstract: [1] Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs have their origin in Bayesian estimation. Methods for batch calibration, despite major recent advances, appear to lack the flexibility required to treat uncertainties in the current system as new information is received. Methods based on sequential Bayesian estimation seem better able to take advantage of the temporal organization and structure of information, so that better compliance of the model output with observations can be achieved. Such methods provide platforms for improved uncertainty assessment and estimation of hydrologic model components, by providing more complete and accurate representations of the forecast and analysis probability distributions. This paper introduces particle filtering as a sequential Bayesian filtering having features that represent the full probability distribution of predictive uncertainties. Particle filters have, so far, generally been used to recursively estimate the posterior distribution of the model state; this paper investigates their applicability to the approximation of the posterior distribution of parameters. The capability and usefulness of particle filters for adaptive inference of the joint posterior distribution of the parameters and state variables are illustrated via two case studies using a parsimonious conceptual hydrologic model.