characterization of interstellar clouds - usm home filej. stutzki, kosma sept. 15th 2010 ag 2010,...

Sept. 15th 2010J. Stutzki, KOSMA AG 2010, Bonn, Splinter ISM, Characterization of Interstellar Cloud Structure

Characterization of Interstellar Clouds

Jürgen Stutzki, I. Phykalisches Institut, Universität zu Kölnnote: - a 7 minute talk can not avoid to be selective and biased... - focus on dense ISM/star formation - short overview/review only ...

OutlineContext and Concepts

Observables and Physical Description

Classification of Methods

new results

Outlook



Jürgen Stutzki, I. Phykalisches Institut, Universität zu Kölnnote: - a 7 minute talk can not avoid to be selective and biased... - focus on dense ISM/star formation - short overview/review only...


interstellar clouds- self-similarity over large range of scales- turbulent dynamics- complex chemical and physical processes - magnetic fields/ionization

stars (and planetary systems)

- gravitationally dominated entities- single/binaries and clusters- universal (?) IMF



Jürgen Stutzki, I. Phykalisches Institut, Universität zu Kölnnote: - a 7 minute talk can not avoid to be selective and biased... - focus on dense ISM/star formation - short overview/review, rather than detailed results...


gas clouds- self-similarity over large range of scales- turbulent dynamics- complex chemical and physical processes - magnetic fields/ionization

stars (and planetary systems)

- gravitationally dominated entities- single/binaries and clusters- universal (?) IMF

???

- star formation efficiency- modes - single / clustered - triggered / stochastic- etc.


Observational Basis:

Rosette/SPIRE 350 μm, DiFransesco et al. 2010Cygnus 13 CO/FCRAO, Schneider et al. 2010

HI 21 cm Canadian GalPlan Survey, figure from Douglas et al. 2003

CO maps with array receivers

HI interferometer maps

dust bolometer maps (SPIRE!)







dust bolometer maps (SPIRE!)Spectral Information!







dust bolometer maps (SPIRE!)Spectral Information!

• sophisticated data reduction• removal of systematics

• error beam• spatial filtering (obs-mode)


spectral line data: data cubes and derived quantities

phase space distribution

hydrodynamic description density

(flow) velocity

spectra (optically thin, thermalized limit)

0th-moment: integrated intensity

↔ column density1st -moment: velocity centroid (normalized)

↔ density weighted l.o.s. average of

l.o.s.-component of flow velocity

unnormalized (modified) velocity centroid

2nd -moment: line width

n r ; v

n r =∫d 3vecv n r ;v

u r =1

nr ∫d3v v n r ; v

I R ;v z =∫d 2 V∫dz n R ,z ; V ,v zN R =∫dv z I R ,v z

=∫dz n R ,z

v c R =1

N R ∫dv z v z I R ;v z

=1

N R ∫d3v∫dz v z n R ,z ; v

=1

N R ∫dz uz R ,z n R ,z

v c R =N R v c R

=∫dz uz R ,z n R ,z

v R =1

N R ∫dz u u zz n R ,z


spectral line data: data cubes and derived quantities

phase space distribution

hydrodynamic description density

(flow) velocity

spectra (optically thin, thermalized limit)

0th-moment: integrated intensity

↔ column density1st -moment: velocity centroid (normalized)

↔ density weighted l.o.s. average of

l.o.s.-component of flow velocity

unnormalized (modified) velocity centroid

2nd -moment: line width

n r ; v

n r =∫d 3vecv n r ;v

u r =1

nr ∫d3v v n r ; v

I R ;v z =∫d 2 V∫dz n R ,z ; V ,v zN R =∫dv z I R ,v z

=∫dz n R ,z

v c R =1

N R ∫dv z v z I R ;v z

=1

N R ∫d3v∫dz v z n R ,z ; v

=1

N R ∫dz uz R ,z n R ,z

v c R =N R v c R

=∫dz uz R ,z n R ,z

v R =1

N R ∫dz u u zz n R ,z

dust continuum observations


Cloud structure analysis: methodsspatial structure (integrated intensity)

indirectexcitation density vs. average density ----> density contrast (CS vs. CO, ....)

excitation temperature vs. brightness temperature ----> filling factor (NH3, ....)

clump identificationeye-ball identification in large scale maps (Solomon et al. 1985; ...)

GAUSSCLUMPS (Stutzki & Güsten, 1990; ....)

CLUMPFIND (Williams et al. 1994; ...),

→ clump mass distribution

→ mass-size-, mass-linewidths-relation

fractal measures & hierarchical structuretree-analysis / dendograms (Houlahan & Scalo 1992; ...; Rosolowky et al. 2008; ...)

area – perimeter relation (Falgarone, Phillips & Walker 1991; ....)

delta-variance / power-spectrum (Stutzki et al. 1998; ...; )

velocity structureintermittency / non-Gaussian line wings (Falgarone & Phillips 1990)

centroid velocity probability density function (Lis,Pety,Phillips & Falgarone 1996; ...)

velocity channel analysis (Lazarian & Pogosyan 2004; ...)

with array receivers, interferometers, and increased continuum sensitivity: new data stimulate their application ....


two apparently conflicting concepts:power-law power spectrum:

density fluctuation at different scales

power spectrum slope2-point correlation etc..

Δ-variancefractal dimensions

ensemble of clumps/fragments:repetition of structures at given scales

mass spectrum: relative number

of “beasts” of given size

mass-size relation: mass/den-sity of “beasts” of given size

mass spectrum dMdM /M¡®; ® = 1:6: : :2:1

mass size rel: M / r¡° ; ° ¼ 2:3

col:dens:powerspectrumP (k) / k¡¯; k = 2:7 : : : 3:1


power-law power spectrum:density fluctuation at different scales







one parameter

two parameters

Stutzki et al., 1998









one parameter

two parameters

observations:What controls these values, and what ensures their matching?

Do we have only one type of beasts?









one parameter

two parameters

observations:What controls these values, and what ensures their matching?

Do we have only one type of beasts?

Larson relation:approx. const. column density









homogeneity: random positioning ↔ gravitational condensation

isotropy: clumps ↔ filamentsmagnetic fields (average and random)









gravity:- velocity dispersion at given scale (Jeans etc. ...)MHD relates density fluctuations and velocity dispersion- modeling ....









2D ↔ 3D ???

gravity:- velocity dispersion at given scale (Jeans etc. ...)MHD relates density fluctuations and velocity dispersion- modeling ....


implications of clump-ensemble scenario:

very counter-intuitive geometrical properties:

example: clumps with a density enhancements > 10 times largest clump

• occupy 2% of the volume• include 21% of the mass• provide 58% of the projected area

mass spectrum dMdM /M¡®; ® = 1:6 : : :2:1

mass size rel: M / r¡° ; ° ¼ 2:3

log(mass)

col. density

density

fractionalmass

fractional area

fractional volume

allows easy implementation for modeling: e.g. PDRs, radiative transfer, ...example: DR21: clumpy ISM / KOSMA-τ PDR model applied to Herschel/HIFI data

(Ossenkopf et al. 2010, WADI GTKP, Herschel first results volume, A&A


recent dust continuum results:

outstanding issue: how different are the structural parameters derived from dust and gas???

new dust continuum data (Bolocam, Herschel/SPIRE) give clump mass spectral indices more consistent with previous results from molecular line data (note: l.o.s. confusion results in steepening!)

examples:

Herschel/SPIRE Rosette (DiFrancesco et al. 2010) α = 1.80 - 1.82

Bolocam Central Molecular Zone (Bally et al. 2010) α = 2.1 - 2.3

very sensitive on details of method applied ...

and/but : core mass spectra seem to be steeper ...

Rosette/SPIRE 350 μm, DiFransesco et al. 2010


Summary and Outlook:

observational data need large range of spatial scales and careful correction for instrumental effects

methods applied are getting mature and give consistent results

modeling starts to get realistic enough to allow an equivalent analysis (simulated observations!) and comparison with observations

emergent picture:

power law power spectral index is always close to 2.8

clump mass spectra always close to 1.8 to 2.0 (core spectra steeper!)

Central open questions: (in particular also for “Schwerpunkt ISM”)

understand transition from clump mass spectrum → core mass spectrum → IMF

understand implications of filamentary ↔ clumpy structure (2D – 3D)

systematically compare best models with observations (also to calibrate methods)

Let's hope that nature has a simply, yet to be understood, underlying scheme!

characterization of interstellar clouds - usm home filej. stutzki, kosma sept. 15th 2010 ag 2010,...

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