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