13,801 research outputs found
Stellar hydrodynamical modeling of dwarf galaxies: simulation methodology, tests, and first results
Cosmological simulations still lack numerical resolution or physical
processes to simulate dwarf galaxies in sufficient details. Accurate numerical
simulations of individual dwarf galaxies are thus still in demand. We aim at
(i) studying in detail the coupling between stars and gas in a galaxy,
exploiting the so-called stellar hydrodynamical approach, and (ii) studying the
chemo-dynamical evolution of individual galaxies starting from
self-consistently calculated initial gas distributions. We present a novel
chemo-dynamical code in which the dynamics of gas is computed using the usual
hydrodynamics equations, while the dynamics of stars is described by the
stellar hydrodynamics approach, which solves for the first three moments of the
collisionless Boltzmann equation. The feedback from stellar winds and dying
stars is followed in detail. In particular, a novel and detailed approach has
been developed to trace the aging of various stellar populations, which enables
an accurate calculation of the stellar feedback depending on the stellar age.
We build initial equilibrium models of dwarf galaxies that take gas
self-gravity into account and present different levels of rotational support.
Models with high rotational support develop prominent bipolar outflows; a
newly-born stellar population in these models is preferentially concentrated to
the galactic midplane. Models with little rotational support blow away a large
fraction of the gas and the resulting stellar distribution is extended and
diffuse. The stellar dynamics turns out to be a crucial aspect of galaxy
evolution. If we artificially suppress stellar dynamics, supernova explosions
occur in a medium heated and diluted by the previous activity of stellar winds,
thus artificially enhancing the stellar feedback (abridged).Comment: 22 pages, 19 figures, accepted for publication in Astronomy &
Astrophysic
Dust Emissivity in the Far-Infrared
We have derived the dust emissivity in the Far-Infrared (FIR) using data
available in the literature. We use two wavelength dependences derived from
spectra of Galactic FIR emission (Reach et al. 1995). A value for the
emissivity, normalised to the extinction efficiency in the V band, has been
retrieved from maps of Galactic FIR emission, dust temperature and extinction
(Schlegel et al. 1998).
Our results are similar to other measurements in the Galaxy but only
marginally consistent with the widely quoted values of Hildebrand (1983)
derived on one reflection nebula. The discrepancy with measurements on other
reflection nebulae (Casey 1991) is higher and suggests a different grain
composition in these environments with respect to the diffuse interstellar
medium.
We measure dust masses for a sample of six spiral galaxies with FIR
observations and obtain gas-to-dust ratios close to the Galactic value.Comment: 5 pages, 1 ps file, A&A letter accepte
Order-disorder phase change in embedded Si nano-particles
We investigated the relative stability of the amorphous vs crystalline
nanoparticles of size ranging between 0.8 and 1.8 nm. We found that, at
variance from bulk systems, at low T small nanoparticles are amorphous and they
undergo to an amorphous-to-crystalline phase transition at high T. On the
contrary, large nanoparticles recover the bulk-like behavior: crystalline at
low T and amorphous at high T. We also investigated the structure of
crystalline nanoparticles, providing evidence that they are formed by an
ordered core surrounded by a disordered periphery. Furthermore, we also provide
evidence that the details of the structure of the crystalline core depend on
the size of the nanoparticleComment: 8 pages, 5 figure
ISO observations of spirals: modelling the FIR emission
ISO observations at 200 micron have modified our view of the dust component
in spiral galaxies. For a sample of seven resolved spirals we have retrieved a
mean temperature of 20K, about 10K lower than previous estimates based on IRAS
data at shorter wavelengths. Because of the steep dependence of far-infrared
emission on the dust temperature, the dust masses inferred from ISO fluxes are
a factor of 10 higher than those derived from IRAS data only, leading to
gas-to-dust ratios close to the value observed in the Galaxy. The scale-length
of the 200 micron emission is larger than for the IRAS 100 micron emission,
with colder dust at larger distances from the galactic centre, as expected if
the interstellar radiation field is the main source of dust heating. The 200
micron scale-length is also larger than the optical, for all the galaxies in
the sample. This suggests that the dust distribution is more extended than that
of the stars.A model of the dust heating is needed to derive the parameters of
the dust distribution from the FIR emission. Therefore, we have adapted an
existing radiative transfer code to deal with dust emission. Simulated maps of
the temperature distribution within the dust disk and of the dust emission at
any wavelength can be produced. The stellar spectral energy distribution is
derived from observations in the ultraviolet, optical and near infrared. The
parameters of the dust distribution (scale-lengths and optical depth) are
chosen to reproduce the observed characteristics of the FIR emission, i.e. the
shape of the spectrum, the flux and the spatial distribution. We describe the
application of the model to one of the galaxies in the sample, NGC 6946.Comment: 6 pages, 5 figures. Contribution to the proceedings of the workshop
"ISO Beyond Point Sources" held at VILSPA 14-17 September 199
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Despite the importance of sparsity in many large-scale applications, there
are few methods for distributed optimization of sparsity-inducing objectives.
In this paper, we present a communication-efficient framework for
L1-regularized optimization in the distributed environment. By viewing
classical objectives in a more general primal-dual setting, we develop a new
class of methods that can be efficiently distributed and applied to common
sparsity-inducing models, such as Lasso, sparse logistic regression, and
elastic net-regularized problems. We provide theoretical convergence guarantees
for our framework, and demonstrate its efficiency and flexibility with a
thorough experimental comparison on Amazon EC2. Our proposed framework yields
speedups of up to 50x as compared to current state-of-the-art methods for
distributed L1-regularized optimization
Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017)
The large scale of scholarly publications poses a challenge for scholars in
information seeking and sensemaking. Bibliometrics, information retrieval (IR),
text mining and NLP techniques could help in these search and look-up
activities, but are not yet widely used. This workshop is intended to stimulate
IR researchers and digital library professionals to elaborate on new approaches
in natural language processing, information retrieval, scientometrics, text
mining and recommendation techniques that can advance the state-of-the-art in
scholarly document understanding, analysis, and retrieval at scale. The BIRNDL
workshop at SIGIR 2017 will incorporate an invited talk, paper sessions and the
third edition of the Computational Linguistics (CL) Scientific Summarization
Shared Task.Comment: 2 pages, workshop paper accepted at the SIGIR 201
Programmable purification of type-I polarization-entanglement
We suggest and demonstrate a scheme to compensate spatial and spectral
decoherence effects in the generation of polarization entangled states by
type-I parametric downconversion. In our device a programmable spatial light
modulator imposes a polarization dependent phase-shift on different spatial
sections of the overall downconversion output and this effect is exploited to
realize an effective purification technique for polarization entanglement.Comment: published versio
Estimating quantum chromatic numbers
We develop further the new versions of quantum chromatic numbers of graphs
introduced by the first and fourth authors. We prove that the problem of
computation of the commuting quantum chromatic number of a graph is solvable by
an SDP algorithm and describe an hierarchy of variants of the commuting quantum
chromatic number which converge to it. We introduce the tracial rank of a
graph, a parameter that gives a lower bound for the commuting quantum chromatic
number and parallels the projective rank, and prove that it is multiplicative.
We describe the tracial rank, the projective rank and the fractional chromatic
numbers in a unified manner that clarifies their connection with the commuting
quantum chromatic number, the quantum chromatic number and the classical
chromatic number, respectively. Finally, we present a new SDP algorithm that
yields a parameter larger than the Lov\'asz number and is yet a lower bound for
the tracial rank of the graph. We determine the precise value of the tracial
rank of an odd cycle.Comment: 34 pages; v2 has improved presentation based after referees'
comments, published versio
CoCoA: A General Framework for Communication-Efficient Distributed Optimization
The scale of modern datasets necessitates the development of efficient
distributed optimization methods for machine learning. We present a
general-purpose framework for distributed computing environments, CoCoA, that
has an efficient communication scheme and is applicable to a wide variety of
problems in machine learning and signal processing. We extend the framework to
cover general non-strongly-convex regularizers, including L1-regularized
problems like lasso, sparse logistic regression, and elastic net
regularization, and show how earlier work can be derived as a special case. We
provide convergence guarantees for the class of convex regularized loss
minimization objectives, leveraging a novel approach in handling
non-strongly-convex regularizers and non-smooth loss functions. The resulting
framework has markedly improved performance over state-of-the-art methods, as
we illustrate with an extensive set of experiments on real distributed
datasets
Coarse graining of master equations with fast and slow states
We propose a general method for simplifying master equations by eliminating
from the description rapidly evolving states. The physical recipe we impose is
the suppression of these states and a renormalization of the rates of all the
surviving states. In some cases, this decimation procedure can be analytically
carried out and is consistent with other analytical approaches, like in the
problem of the random walk in a double-well potential. We discuss the
application of our method to nontrivial examples: diffusion in a lattice with
defects and a model of an enzymatic reaction outside the steady state regime.Comment: 9 pages, 9 figures, final version (new subsection and many minor
improvements
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