Statistical properties of dust far-infrared emission

Context. Far-infrared dust emission has a self-similar structure which reveals the complex dynamical processes that shape the interstellar medium. The description of the statistical properties of this emission gives important constraints on the physics of the interstellar medium but it is also a useful way to estimate the contamination of diffuse interstellar emission in the cases where it is considered a nuisance. Aims. The main goals of this analysis of the power spectrum and non-Gaussian properties of far-infrared dust emission are 1) to estimate the power spectrum of interstellar matter density in three dimensions; 2) to review and extend previous estimates of the cirrus noise due to dust emission; and 3) to produce simulated dust emission maps that reproduce the observed statistical properties. Methods. To estimate the statistical properties of dust emission we analyzed the power spectrum and wavelet decomposition of 100  μ m IRIS data (an improved version of the IRAS data) over 55% of the sky. The simulation of realistic infrared emission maps is based on modified Gaussian random fields. Results. The main results are the following. 1) The cirrus noise level as a function of brightness has been previously overestimated. It is found to be proportional to $\langle I\rangle $ instead of $\langle I\rangle^{1.5}$, where $\langle I\rangle $ is the local average brightness at 100  μ m. This scaling is in accordance with the fact that the brightness fluctuation level observed at a given angular scale on the sky is the sum of fluctuations of increasing amplitude with distance on the line of sight. 2) The spectral index of dust emission at scales between 5 arcmin and 12.5° is $\langle\gamma\rangle=-2.9$ on average but shows significant variations over the sky. Bright regions have systematically steeper power spectra than diffuse regions. 3) The skewness and kurtosis of brightness fluctuations are high, indicative of strong non-Gaussianity. Unlike the standard deviation of the fluctuations, the skewness and kurtosis do not depend significantly on brightness, except in bright regions (>10 MJy sr -1 ) where they are systematically higher, probably due to contrasted structures related to star formation activity. 4) Based on our characterization of the 100  μ m power spectrum we provide a prescription of the cirrus confusion noise as a function of wavelength and scale. 5) Finally we present a method based on a modification of Gaussian random fields to produce simulations of dust maps which reproduce the power spectrum and non-Gaussian properties of interstellar dust emission.

[1]  C. Walker,et al.  The edges of molecular clouds: Fractal boundaries and density structure , 1991 .

[2]  Fionn Murtagh,et al.  Automatic Noise Estimation from the Multiresolution Support , 1998 .

[3]  L. Tenorio,et al.  Numerical Simulation of Non‐Gaussian Random Fields with Prescribed Marginal Distributions and Cross‐Correlation Structure. II. Multivariate Random Fields , 2002, astro-ph/0207311.

[4]  B. Draine,et al.  Infrared Emission from Interstellar Dust Ii. the Diffuse Interstellar Medium , 2000 .

[5]  David J. Schlegel,et al.  Extrapolation of Galactic Dust Emission at 100 Microns to Cosmic Microwave Background Radiation Frequencies Using FIRAS , 1999, astro-ph/9905128.

[6]  P. Dewdney,et al.  A high-resolution 21 centimeter line study of infrared cirrus , 1992 .

[7]  N. Aghanim,et al.  Non-Gaussianity: Comparing wavelet and Fourier based methods , 2003 .

[8]  M. Wilson.  The 3s 3p 4p configurations in Al I and Si II , 1983 .

[9]  Brigitte Kaldeich,et al.  From ground-based to space-borne sub-mm astronomy , 1990 .

[10]  A. Abergel,et al.  Evolution of dust properties in an interstellar filament , 2003 .

[11]  George Helou,et al.  The confusion limits to the sensitivity of submillimeter telescopes , 1990 .

[12]  F. Boulanger,et al.  High resolution 21 cm mapping of the Ursa Major Galactic cirrus: Power spectra of the high-latitude HI gas , 2003, astro-ph/0306570.

[13]  B. Elmegreen.  A Fractal Origin for the Mass Spectrum of Interstellar Clouds. II. Cloud Models and Power-Law Slopes , 1996, astro-ph/0112528.

[14]  A. Goodman,et al.  The Effects of Noise and Sampling on the Spectral Correlation Function , 2000, astro-ph/0010344.

[15]  James G. Ingalls,et al.  Structure and Colors of Diffuse Emission in the Spitzer Galactic First Look Survey , 2004 .

[16]  P. Hennebelle,et al.  Thermal condensation in a turbulent atomic hydrogen flow , 2004 .

[17]  M. Bershady,et al.  SparsePak: A Formatted Fiber Field Unit for the WIYN Telescope Bench Spectrograph. I. Design, Construction, and Calibration , 2004, astro-ph/0403456.

[18]  On the Use of Fractional Brownian Motion Simulations to Determine the Three-dimensional Statistical Properties of Interstellar Gas , 2003, astro-ph/0304539.

[19]  C. Brunt,et al.  Interstellar Turbulence: I. Retrieval of Velocity Field Statistics , 2000, astro-ph/0011200.

[20]  Soojong Pak,et al.  Far-infrared detection limits¿ I. Sky confusion due to Galactic cirrus , 2005 .

[21]  F. Bensch,et al.  Quantification of molecular cloud structure using the Delta -variance , 2001 .

[22]  Michael L. Norman,et al.  Can We Trust the Dust? Evidence of Dust Segregation in Molecular Clouds , 2006, astro-ph/0607028.

[23]  T. N. Gautier,et al.  A calculation of confusion noise due to infrared cirrus , 1992 .

[24]  M. Pérault,et al.  Diffuse infrared emission from the galaxy. I: Solar neighborhood , 1988 .

[25]  Guilaine Lagache,et al.  IRIS : A NEW GENERATION OF IRAS MAPS , 2005 .

[26]  Edward L. Wright.  Angular Power Spectra of the COBE DIRBE Maps , 1998 .

[27]  C. Brunt.  Large-Scale Turbulence in Molecular Clouds , 2003 .

[28]  P. Ábrahám,et al.  Small-scale structure of the galactic cirrus emission , 2002, astro-ph/0212094.

[29]  I. Goldman.  Interpretation of the Spatial Power Spectra of Neutral Hydrogen in the Galaxy and in the Small Magellanic Cloud , 2000, astro-ph/0005023.

[30]  Horváth,et al.  A search for scale-dependent morphology in five molecular cloud complexes , 1989 .

[31]  G. Lagache,et al.  Power spectrum of the cosmic infrared background at 60 and 100 μm with IRAS , 2002, astro-ph/0207312.

[32]  R. Sault,et al.  The large‐scale HI structure of the Small Magellanic Cloud , 1999 .

[33]  K. Gorski,et al.  HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere , 2004, astro-ph/0409513.

[34]  J. Jewell.  A Statistical Characterization of Galactic Dust Emission as a Non-Gaussian Foreground of the Cosmic Microwave Background , 2001 .

[35]  E. L. Wright,et al.  The COBE Diffuse Infrared Background Experiment Search for the Cosmic Infrared Background. I. Limits and Detections , 1998, astro-ph/9806167.

[36]  S. Lazarian.  Velocity and density spectra of the small magellanic cloud , 2001, astro-ph/0102191.

[37]  D. Bazell,et al.  Fractal structure of interstellar cirrus , 1988 .

[38]  J. Dickey,et al.  Southern Galactic Plane Survey Measurements of the Spatial Power Spectrum of Interstellar H I in the Inner Galaxy , 2001, astro-ph/0107604.

[39]  Garching,et al.  Numerical Simulation of Non‐Gaussian Random Fields with Prescribed Correlation Structure , 2001, astro-ph/0105107.

[40]  M. Juvela,et al.  The Spectral Correlation Function of Molecular Clouds: A Statistical Test for Theoretical Models , 2002, astro-ph/0211135.