31,018 research outputs found
Mid - infrared interferometry of massive young stellar objects II Evidence for a circumstellar disk surrounding the Kleinmann - Wright object
The formation scenario for massive stars is still under discussion. To
further constrain current theories, it is vital to spatially resolve the
structures from which material accretes onto massive young stellar objects
(MYSOs). Due to the small angular extent of MYSOs, one needs to overcome the
limitations of conventional thermal infrared imaging, regarding spatial
resolution, in order to get observational access to the inner structure of
these objects.We employed mid - infrared interferometry, using the MIDI
instrument on the ESO /VLTI, to investigate the Kleinmann - Wright Object, a
massive young stellar object previously identified as a Herbig Be star
precursor. Dispersed visibility curves in the N- band (8 - 13 {\mu}m) have been
obtained at 5 interferometric baselines. We show that the mid - infrared
emission region is resolved. A qualitative analysis of the data indicates a non
- rotationally symmetric structure, e.g. the projection of an inclined disk. We
employed extensive radiative transfer simulations based on spectral energy
distribution fitting. Since SED - only fitting usually yields degenerate
results, we first employed a statistical analysis of the parameters provided by
the radiative transfer models. In addition, we compared the ten best - fitting
self - consistent models to the interferometric observations. Our analysis of
the Kleinmann - Wright Object suggests the existence of a circumstellar disk of
0.1M\odot at an intermediate inclination of 76\circ, while an additional dusty
envelope is not necessary for fitting the data. Furthermore, we demonstrate
that the combination of IR interferometry with radiative transfer simulations
has the potential to resolve ambiguities arising from the analysis of spectral
energy distributions alone.Comment: 12 pages, 22 figures accepted for publication in A&
Gas dynamics in the inner few AU around the Herbig B[e] star MWC297: Indications of a disk wind from kinematic modeling and velocity-resolved interferometric imaging
We present near-infrared AMBER (R = 12, 000) and CRIRES (R = 100, 000)
observations of the Herbig B[e] star MWC297 in the hydrogen Br-gamma-line.
Using the VLTI unit telescopes, we obtained a uv-coverage suitable for aperture
synthesis imaging. We interpret our velocity-resolved images as well as the
derived two-dimensional photocenter displacement vectors, and fit kinematic
models to our visibility and phase data in order to constrain the gas velocity
field on sub-AU scales. The measured continuum visibilities constrain the
orientation of the near-infrared-emitting dust disk, where we determine that
the disk major axis is oriented along a position angle of 99.6 +/- 4.8 degrees.
The near-infrared continuum emission is 3.6 times more compact than the
expected dust-sublimation radius, possibly indicating the presence of highly
refractory dust grains or optically thick gas emission in the inner disk. Our
velocity-resolved channel maps and moment maps reveal the motion of the
Br-gamma-emitting gas in six velocity channels, marking the first time that
kinematic effects in the sub-AU inner regions of a protoplanetary disk could be
directly imaged. We find a rotation-dominated velocity field, where the blue-
and red-shifted emissions are displaced along a position angle of 24 +/- 3
degrees and the approaching part of the disk is offset west of the star. The
visibility drop in the line as well as the strong non-zero phase signals can be
modeled reasonably well assuming a Keplerian velocity field, although this
model is not able to explain the 3 sigma difference that we measure between the
position angle of the line photocenters and the position angle of the dust
disk. We find that the fit can be improved by adding an outflowing component to
the velocity field, as inspired by a magneto-centrifugal disk-wind scenario.Comment: 15 pages, 13 Figure
Estimating Intertemporal Allocation Parameters using Synthetic Residual Estimation
We present a novel structural estimation procedure for models of intertemporal allocation. This is based on modelling expectation errors directly; we refer to it as Synthetic Residual Estimation (SRE). The flexibility of SRE allows us to account for measurement error in consumption and for heterogeneity in discount factors and coefficients of relative risk aversion. An investigation of the small sample properties of the SRE estimator indicates that it dominates GMM estimation of both exact and approximate Euler equations in the case when we have short panels with noisy consumption data. We apply SRE to two panels drawn from the PSID and estimate the joint distribution of the discount factor and the coefficient of relative risk aversion. We reject strongly homogeneity of the discount factors and the coefficient of relative risk aversion. We find that, on average, the more educated are more patient and more risk averse than the less educated. Within education strata, patience and risk aversion are negatively correlated
Preparation and characterization of self assembled polymer nanocomposites
Polymerní nanokompozity na bázi polyhedrálních oligomerních silsesquioxanů (POSS) představují slibnou oblast výzkumu, která potenciálně může využít samouspořádávní při navrhování nových materiálů. Tato diplomová práce popisuje postup přípravy oktafenyl-POSS/PS, oktafenyl-POSS/PMMA a oktamethyl-POSS/PS systémů a charakterizaci jejich termomechanických vlastností v pevné fázi a reologických vlastností v roztoku. Získané výsledky jsou diskutovány s přihlédnutím k teoriím zabývajících se stavem disperze nanočástic.Polymer nanocomposites based on polyhedral oligomeric silsesquioxanes (POSS) are promising field which could potentially utilize self-assembly approach in designing new materials. In this thesis, a preparation protocol of octaphenyl-POSS/PS, octamethyl-POSS/PMMA and octamethyl-POSS/PS systems was described and thermomechanic properties in solid state and rheological properties in solution were investigated. The obtained results are discussed with focus on nanoparticles dispersion state theories.
Next generation sequencing analysis reveals a relationship between rDNA unit diversity and locus number in Nicotiana diploids
© 2012 Matyášek et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited
Atmospheric Retrieval for Super-Earths: Uniquely Constraining the Atmospheric Composition with Transmission Spectroscopy
We present a retrieval method based on Bayesian analysis to infer the
atmospheric compositions and surface or cloud-top pressures from transmission
spectra of exoplanets with general compositions. In this study, we identify
what can unambiguously be determined about the atmospheres of exoplanets from
their transmission spectra by applying the retrieval method to synthetic
observations of the super-Earth GJ 1214b. Our approach to infer constraints on
atmospheric parameters is to compute their joint and marginal posterior
probability distributions using the MCMC technique in a parallel tempering
scheme. A new atmospheric parameterization is introduced that is applicable to
general atmospheres in which the main constituent is not known a priori and
clouds may be present. Our main finding is that a unique constraint of the
mixing ratios of the absorbers and up to two spectrally inactive gases (such as
N2 and primordial H2+He) is possible if the observations are sufficient to
quantify both (1) the broadband transit depths in at least one absorption
feature for each absorber and (2) the slope and strength of the molecular
Rayleigh scattering signature. The surface or cloud-top pressure can be
quantified if a surface or cloud deck is present. The mean molecular mass can
be constrained from the Rayleigh slope or the shapes of absorption features,
thus enabling to distinguish between cloudy hydrogen-rich atmospheres and high
mean molecular mass atmospheres. We conclude, however, that without the
signature of Rayleigh scattering--even with robustly detected infrared
absorption features--there is no reliable way to tell if the absorber is the
main constituent of the atmosphere or just a minor species with a mixing ratio
of <0.1%. The retrieval method leads us to a conceptual picture of which
details in transmission spectra are essential for unique characterizations of
well-mixed atmospheres.Comment: 23 pages, 13 figures, accepted at ApJ, submitted to ApJ on Nov 4,
201
Structural Material Property Tailoring Using Deep Neural Networks
Advances in robotics, artificial intelligence, and machine learning are
ushering in a new age of automation, as machines match or outperform human
performance. Machine intelligence can enable businesses to improve performance
by reducing errors, improving sensitivity, quality and speed, and in some cases
achieving outcomes that go beyond current resource capabilities. Relevant
applications include new product architecture design, rapid material
characterization, and life-cycle management tied with a digital strategy that
will enable efficient development of products from cradle to grave. In
addition, there are also challenges to overcome that must be addressed through
a major, sustained research effort that is based solidly on both inferential
and computational principles applied to design tailoring of functionally
optimized structures. Current applications of structural materials in the
aerospace industry demand the highest quality control of material
microstructure, especially for advanced rotational turbomachinery in aircraft
engines in order to have the best tailored material property. In this paper,
deep convolutional neural networks were developed to accurately predict
processing-structure-property relations from materials microstructures images,
surpassing current best practices and modeling efforts. The models
automatically learn critical features, without the need for manual
specification and/or subjective and expensive image analysis. Further, in
combination with generative deep learning models, a framework is proposed to
enable rapid material design space exploration and property identification and
optimization. The implementation must take account of real-time decision cycles
and the trade-offs between speed and accuracy
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