Exact results for hydrogen recombination on dust grain surfaces

The recombination of hydrogen in the interstellar medium, taking place on surfaces of microscopic dust grains, is an essential process in the evolution of chemical complexity in interstellar clouds. The H_2 formation process has been studied theoretically, and in recent years also by laboratory experiments. The experimental results were analyzed using a rate equation model. The parameters of the surface, that are relevant to H_2 formation, were obtained and used in order to calculate the recombination rate under interstellar conditions. However, it turned out that due to the microscopic size of the dust grains and the low density of H atoms, the rate equations may not always apply. A master equation approach that provides a good description of the H_2 formation process was proposed. It takes into account both the discrete nature of the H atoms and the fluctuations in the number of atoms on a grain. In this paper we present a comprehensive analysis of the H_2 formation process, under steady state conditions, using an exact solution of the master equation. This solution provides an exact result for the hydrogen recombination rate and its dependence on the flux, the surface temperature and the grain size. The results are compared with those obtained from the rate equations. The relevant length scales in the problem are identified and the parameter space is divided into two domains. One domain, characterized by first order kinetics, exhibits high efficiency of H_2 formation. In the other domain, characterized by second order kinetics, the efficiency of H_2 formation is low. In each of these domains we identify the range of parameters in which, the rate equations do not account correctly for the recombination rate. and the master equation is needed.Comment: 23 pages + 8 figure

Brain tumour genetic network signatures of survival

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic, and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH- wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH- mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours, and show that generative network models reveal distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories.Comment: Main article: 52 pages, 1 table, 7 figures. Supplementary material: 13 pages, 11 supplementary figure

Dust Dynamics in Compressible MHD Turbulence

We calculate the relative grain-grain motions arising from interstellar magnetohydrodynamic (MHD) turbulence. The MHD turbulence includes both fluid motions and magnetic fluctuations. While the fluid motions accelerate grains through hydro-drag, the electromagnetic fluctuations accelerate grains through resonant interactions. We consider both incompressive (Alfv\'{e}n) and compressive (fast and slow) MHD modes and use descriptions of MHD turbulence obtained in Cho & Lazarian (2002). Calculations of grain relative motion are made for realistic grain charging and interstellar turbulence that is consistent with the velocity dispersions observed in diffuse gas, including cutoff of the turbulence from various damping processes. We show that fast modes dominate grain acceleration, and can drive grains to supersonic velocities. Grains are also scattered by gyroresonance interactions, but the scattering is less important than acceleration for grains moving with sub-Alfv\'{e}nic velocities. Since the grains are preferentially accelerated with large pitch angles, the supersonic grains will be aligned with long axes perpendicular to the magnetic field. We compare grain velocities arising from MHD turbulence with those arising from photoelectric emission, radiation pressure and H$_{2}$ thrust. We show that for typical interstellar conditions turbulence should prevent these mechanisms from segregating small and large grains. Finally, gyroresonant acceleration is bound to preaccelerate grains that are further accelerated in shocks. Grain-grain collisions in the shock may then contribute to the overabundance of refractory elements in the composition of galactic cosmic rays.Comment: 15 pages, 17 figure

Density of states in random lattices with translational invariance

We propose a random matrix approach to describe vibrational excitations in disordered systems. The dynamical matrix M is taken in the form M=AA^T where A is some real (not generally symmetric) random matrix. It guaranties that M is a positive definite matrix which is necessary for mechanical stability of the system. We built matrix A on a simple cubic lattice with translational invariance and interaction between nearest neighbors. We found that for certain type of disorder phonons cannot propagate through the lattice and the density of states g(w) is a constant at small w. The reason is a breakdown of affine assumptions and inapplicability of the elasticity theory. Young modulus goes to zero in the thermodynamic limit. It strongly reminds of the properties of a granular matter at the jamming transition point. Most of the vibrations are delocalized and similar to diffusons introduced by Allen, Feldman et al., Phil. Mag. B v.79, 1715 (1999).Comment: 4 pages, 5 figure

Sensitivity analyses of dense cloud chemical models

Because of new telescopes that will dramatically improve our knowledge of the interstellar medium, chemical models will have to be used to simulate the chemistry of many regions with diverse properties. To make these models more robust, it is important to understand their sensitivity to a variety of parameters. In this article, we report a study of the sensitivity of a chemical model of a cold dense core, with homogeneous and time-independent physical conditions, to variations in the following parameters: initial chemical inventory, gas temperature and density, cosmic-ray ionization rate, chemical reaction rate coefficients, and elemental abundances. From the results of the parameter variations, we can quantify the sensitivity of the model to each parameter as a function of time. Our results can be used in principle with observations to constrain some parameters for different cold clouds. We also attempted to use the Monte Carlo approach with all parameters varied collectively. Within the parameter ranges studied, the most critical parameters turn out to be the reaction rate coefficients at times up to 4e5 yr and elemental abundances at later times. At typical times of best agreement with observation, models are sensitive to both of these parameters. The models are less sensitive to other parameters such as the gas density and temperature. The improvement of models will require that the uncertainties in rate coefficients of important reactions be reduced. As the chemistry becomes better understood and more robust, it should be possible to use model sensitivities concerning other parameters, such as the elemental abundances and the cosmic ray ionization rate, to yield detailed information on cloud properties and history. Nevertheless, at the current stage, we cannot determine the best values of all the parameters simultaneously based on purely observational constraints.Comment: Accepted for publication in Astronomy & Astrophysic

A Coupled Dynamical and Chemical Model of Starless Cores of Magnetized Molecular Clouds: I. Formulation and Initial Results

We develop a detailed chemical model for the starless cores of strongly magnetized molecular clouds, with the ambipolar diffusion-driven dynamic evolution of the clouds coupled to the chemistry through ion abundances. We concentrate on two representative model clouds in this initial study, one with magnetic fields and the other without. The model predictions on the peak values and spatial distributions of the column densities of CO, CCS, N$_2$H$^+$ and HCO$^+$ are compared with those observationally inferred for the well-studied starless core L1544, which is thought to be on the verge of star formation. We find that the magnetic model, in which the cloud is magnetically supported for several million years before collapsing dynamically, provides a reasonable overall fit to the available data on L1544; the fit is significantly worse for the non-magnetic model, in which the cloud collapses promptly. The observed large peak column density for N$_2$H$^+$ and clear central depression for CCS favor the magnetically-retarded collapse over the free-fall collapse. A relatively high abundance of CCS is found in the magnetic model, resulting most likely from an interplay of depletion and late-time hydrocarbon chemistry enhanced by CO depletion. These initial results lend some support to the standard picture of dense core formation in strongly magnetized clouds through ambipolar diffusion. They are at variance with those of Aikawa et al. (2001) who considered a set of models somewhat different from ours and preferred one in which the cloud collapses more or less freely for L1544.Comment: 25 pages, 7 figures, accepted to Ap

Observations of chemical differentiation in clumpy molecular clouds

We have extensively mapped a sample of dense molecular clouds (L1512, TMC-1C, L1262, Per 7, L1389, L1251E) in lines of HC3N, CH3OH, SO and C^{18}O. We demonstrate that a high degree of chemical differentiation is present in all of the observed clouds. We analyse the molecular maps for each cloud, demonstrating a systematic chemical differentiation across the sample, which we relate to the evolutionary state of the cloud. We relate our observations to the cloud physical, kinematical and evolutionary properties, and also compare them to the predictions of simple chemical models. The implications of this work for understanding the origin of the clumpy structures and chemical differentiation observed in dense clouds are discussed.Comment: 20 pages, 7 figures. Higher quality figures appear in the published journal articl

Physical Origin of the Boson Peak Deduced from a Two-Order-Parameter Model of Liquid

We propose that the boson peak originates from the (quasi-) localized vibrational modes associated with long-lived locally favored structures, which are intrinsic to a liquid state and are randomly distributed in a sea of normal-liquid structures. This tells us that the number density of locally favored structures is an important physical factor determining the intensity of the boson peak. In our two-order-parameter model of the liquid-glass transition, the locally favored structures act as impurities disturbing crystallization and thus lead to vitrification. This naturally explains the dependence of the intensity of the boson peak on temperature, pressure, and fragility, and also the close correlation between the boson peak and the first sharp diffraction peak (or prepeak).Comment: 5 pages, 1 figure, An error in the reference (Ref. 7) was correcte

Spitzer infrared spectrograph point source classification in the Small Magellanic Cloud

The Magellanic Clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused by dust. We have searched the Spitzer Space Telescope data archive for all Infrared Spectrograph (IRS) staring-mode observations of the Small Magellanic Cloud (SMC) and found that 209 Infrared Array Camera (IRAC) point sources within the footprint of the Surveying the Agents of Galaxy Evolution in the Small Magellanic Cloud (SAGE-SMC) Spitzer Legacy programme were targeted, within a total of 311 staring-mode observations. We classify these point sources using a decision tree method of object classification, based on infrared spectral features, continuum and spectral energy distribution shape, bolometric luminosity, cluster membership and variability information. We find 58 asymptotic giant branch (AGB) stars, 51 young stellar objects, 4 post-AGB objects, 22 red supergiants, 27 stars (of which 23 are dusty OB stars), 24 planetary nebulae (PNe), 10 Wolf–Rayet stars, 3 H II regions, 3 R Coronae Borealis stars, 1 Blue Supergiant and 6 other objects, including 2 foreground AGB stars. We use these classifications to evaluate the success of photometric classification methods reported in the literature

Grain Surface Models and Data for Astrochemistry

AbstractThe cross-disciplinary field of astrochemistry exists to understand the formation, destruction, and survival of molecules in astrophysical environments. Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. A broad consensus has been reached in the astrochemistry community on how to suitably treat gas-phase processes in models, and also on how to present the necessary reaction data in databases; however, no such consensus has yet been reached for grain-surface processes. A team of ∼25 experts covering observational, laboratory and theoretical (astro)chemistry met in summer of 2014 at the Lorentz Center in Leiden with the aim to provide solutions for this problem and to review the current state-of-the-art of grain surface models, both in terms of technical implementation into models as well as the most up-to-date information available from experiments and chemical computations. This review builds on the results of this workshop and gives an outlook for future directions