Filamentary Structures in Molecular Clouds and their connection

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Filamentary Structures in Molecular Clouds and their connection Powered By Docstoc
					          Filaments in the Galaxy: Their properties
          and their connection with Star Formation

                Eugenio Schisano – IFSI INAF - Roma




In collaboration with D.Polychroni, S.Molinari, D.Elia, M.Pestalozzi,
            G.Busquet, K. Rygl, N.Billot, M. Veneziani,
                and other members of Hi-GAL Team



Background: Hi-GAL map of l=299° observed at 250 μm
             Outline




MIPS 24 μm
MIPS 24 μm
SPIRE 250 μm
MIPS 24 μm
SPIRE 250 μm
                                      Pre-Herschel
Observations in optical, near-IR, submm and in CO rotational lines
show that gas and dust in the star forming complexes are arranged
in a filamentary pattern.

Either dense cores or more evolved objects, e.g. protostars, are found
along such structures.

Lynds 1962, Scheider & Elmegreen 1979, Mitchell et al.2001, Loren 1989,
Motte et al. 1998, Hartmann et al.2002, Hatchell et al.2005, Lada et al.2007,
Goldsmith et al.2008, and many other works
                                                                      L1495

For Example:
                                                           L1521
Taurus Complex in 13CO J = 1 - 0
3 parallel filaments plus other
coming out from the “hub” L1495                                       L1508


Figure from Goldsmith et al. 2008
(colors traces integrated intensities in 3-5 km s-1
blue, 5-7 km s-1 green 7-9 km s-1 red)
                                                           L1536
                                                                      5 pc
Herschel reminded us that the Molecular Clouds are
strongly filamentary (Andrè et al. 2010, Molinari et al.2010).


Even if it is not a new idea, the scientific comunity is
finally accepting that the spheroidal models are not
adequate to describe star forming molecular clouds
on large scales.

Most of the recent numerical simulations are able to
predict qualitatively the formation of a filament or a
network of filaments in the process of forming stars
(Nagai et al. 1998, Klessen 2001, Padoan et al. 2001, Klessen et al.
2004, Bernarjee et al. 2006, Li & Nakamura 2006, Vazques-Semadeni
et al. 2007, Heitsch et al. 2008).

So far Quantitative studies regard the cores (i.e. Offner & Krumoltz 2009)
Filaments are ubiquitous and represent an initial stage
of star formation that is not well studied nor understood.

Why do Molecular Clouds have those shapes?

-) Result of the collision of randomly directed flows like large scale
turbulence (Padoan et al.2001, Klessen et al. 2004).

-) Collision of uniform cylinders of gas along their symmetry axis
(Vasquez-Semademi et al. 2007, Heitsch et al. 2008)

-) Magnetically dominated model, where the self-gravity pulls gas to
the midplane and ambipolar diffusion allows gravitational
instabilities (Nakamura & Li 2008)


Moreover, has the filamentary pattern any connection
with the final cores/clumps ?
       L 59 field

       SPIRE 250 μm map


       Highly structured
       Emission

       Difficulties in the
       extraction of
       compact objects




120’
                                                 L 59 field

                                                 SPIRE 250 μm map


                                                 Highly structured
                                                 Emission

                                                 Difficulties in the
                                                 extraction of
                                                 compact objects

                                                 With the aid of an
                                                 high pass band
                                                 filter the emission
                                                 Is damped.

                                                 Dense compact
                                                 structures start to
                                                 stick out!
Image credit: Molinari 2010   Candidate source
Qualitatively the sources cluster on the filaments.
Our goal is to use the potential of Hi-GAL survey to build
up a catalog of filamentary structures identified on the
GP maps, for which we determine:



Morphological Properties (position, length and width) linked to the
filament formation process (sweeping/compression of matter, fragmentation
etc).


Physical Properties (mass, virial mass per unit length, temperature,
column density) Mechanisms active in such structures.
Study the stability of those structures.
Comparison with classical filament models (Ostriker et al. 1964,
Fiege & Pudritz 2000,2004) of bounded, self-gravitating, structure.

Correlation with the embedded cores (core shapes, core elongation,
cores reciprocal distances – scales of filament fragmentation)
                      Measure          Search for
Identify a sample
                    Morphological   correlations with
  of filaments in
                    and Physical     compact object
  unbiased way
                     properties        properties
                 Filament identification Algorithm
Image processing techniques to develop algorithms able to identify the
filamentary structures. It is a classical “Pattern recognition problem” .

Different approaches can be found in literature:

1) Optimal filtering:

Attempt to find linear image structures from optimal edge detection, like
Canny detector (see also Canny 1986).

2) Determination of the local properties of the image

    - Hessian Based methods:

Compute the Hessian matrix and its eigenvalues to classify pixels on the basis
of how cylinder like are the local intensities.

    - Topological analysis, like DisPerSE code
                                    (see Sousbie 2010, Arzoumanian’s talk)

3) Other methods based on statistical estimators
                 Filament identification Algorithm
Image processing techniques to develop algorithms able to identify the
filamentary structures. It is a classical “Pattern recognition problem” .

Different approaches can be found in literature:

1) Optimal filtering:

Attempt to find linear image structures from optimal edge detection, like
Canny detector (see also Canny 1986).

2) Determination of the local properties of the image

    - Hessian Based methods:

Compute the Hessian matrix and its eigenvalues to classify pixels on the basis
of how cylinder like are the local intensities.

    - Topological analysis, like DisPerSE code
                                    (see Sousbie 2010, Arzoumanian’s talk)

3) Other methods based on statistical estimators
                 Filament identification Algorithm
Image processing techniques to develop algorithms able to identify the
filamentary structures. It is a classical “Pattern recognition problem” .

Different approaches can be found in literature:

1) Optimal filtering:

Attempt to find linear image structures from optimal edge detection, like
Canny detector (see also Canny 1986).

2) Determination of the local properties of the image

    - Hessian Based methods:

Compute the Hessian matrix and its eigenvalues to classify pixels on the basis
of how cylinder like are the local intensities.

    - Topological analysis, like DisPerSE code
                                    (see Sousbie 2010, Arzoumanian’s talk)

3) Other methods based on statistical estimators
          Filament identification in a nutshell - 1

Filament: Structure that is concave down along two
different principal axes and is almost flat in the other one.

Method used on cosmological datasets to identify underlying structures
(Aragon-Calvo et al.2007, Bond et al 2010)

Elongated cylindrical-like patterns are traced by the lowest
eigenvalue (λ1 << λ2) and the eigenvectors (A1,A2) of the
Hessian matrix computed in each pixel.

Extended not elongated regions are rejected by criteria on
the highest eigenvalue and the eigenvectors.

However the method may miss structures with large
variations of emission along the axis of the cylinder
( flat condition along filament axis often are not fulfilled )
          Filament identification in a nutshell - 2



We complements the Hessian approach with an Edge Dectector-type method.

We compute the eigenvalues of the Hessian Matrix and determine a threshold
value to identify the pixels belonging to the filament.

Assuming that the Filament is symmetric in its shape we apply the
morphological operators of erosion (Gonzales & Wood 2002) to determine an
estimate of the central “Spine” (see also Qu & Shih 2005)

All the points of the “Spine” are then connected through a Minimum Spanning
Tree (MST) that give the unique path linking together all the pixels of the spine.
Input Image
                 Filament identification in a nutshell Matrix
                                    Eigenvalues of the Hessian
                                                               -2
 We compute the eigenvalues of the Hessian Matrix and determine a threshold
 value to identify the pixels belonging to the filament.

 Assuming that the Filament is symmetric in its shape we apply the
 morphological operators of erosion (Gonzales & Wood 2002) to determine an
 estimate of the central “Spine” (see also Qu & Shih 2005)
Simulation Filament with 2 sources
Contrast Filament – Background ~ 9




 Thresholded mask                      Application Morphological Operator
                        Filament Simulations

                                                 We build up a simple simulation of
                                                 of linear structures.

                                                 The filaments are simulated by
                                                 computing spines with moderate
                                                 curvatures, idealized radial profile
                                                 with a power-law decrement with
                                                 exponent between 2 and 4.
                                                 Variable intensity along the spine
                                                 Introduced to take in account the
                                                 presence of sources embedded in
                                                 the structure.




Structures of different widths and lengths are
distributed on a FBM map (Stutzki et al. 1998)          Fluctuation on the Spine
simulating the emission of the diffuse ISM
Filament Simulations

              Black lines define the spine of
              the linear structures identified
              by mixing Hessian Diagonalization
              methods with “edge detectors”
              approaches.


              Areas are recovered within 15%
              the simulated region, projected
              lengths with accuracy of 5 to 10%.




                    Fluctuation on the Spine
                      Measure          Search for
Identify a sample
                    Morphological   correlations with
  of filaments in
                    and Physical     compact object
  unbiased way
                     properties        properties
               A case of study - L59 region
                                     Vulpecula region
MIPS 24 μm


                                    Well know filamentary
                                    regions.

                                    Vulpecula OB1 association.


         NGC 6823




 10’                               See Billot et al. 2010,
                                   Elia et al.2010
                   A case of study - L59 region

PACS 160 μm                                SPIRE 250 μm
                                           SPIRE 250
                                           μm




10’                                         10’



Using the photometry package CuTEx (Molinari et al. 2010) we identified the compact
sources in the Vulpecula field. Improvement in the extraction techniques allowed us to
find 401 candidates with robust indipendent detection in the 3 consecutive bands
of 160, 250, 350 μm.
A case of study - L59 region

             SPIRE 250   Source detected
             μm          By CuTEx in at least
                         3 phtoometryc band




              10’
           A case of study - L59 region
AV (mag)
                        SPIRE 250   We computed the Column
                        μm          Density and Temperature
                                    maps fitting modified gray
                                    body functions by pixel
                                    by pixel.




                                    Quite a few filaments are
                                    recognized on the column
                                    density map.
                         10’




 10’
A case of study - L59 region
                       Temperature Map
           A case of study - L59 region
AV (mag)
                                 We identied 50 filaments
                                 that have a mean length
                                 longer than 200”.
                                 ( > 2 pc @ 2.2 kpc)

                                 Another 50 filaments are
                                 classified as candidates
                                 coherent structures since
                                 they are enough extended
                                 to be identified, but have
                                 shorter lengths than 200”.


                                 Original masks includes
                                 hundreds of small scale
                                 structures, highlighted by
                                 the derivative computatio.n
 10’
                                Measure                   Search for
   Identify a sample
                              Morphological            correlations with
     of filaments in
                              and Physical              compact object
     unbiased way
                               properties                 properties




Low extinction regions with mean values of ~ 3 AV. Pixels associated with
sources are not taken into account in those computation.
Background is removed fitting neighbors pixels.
Mean Temperature are roughtly constant.
No systematic difference between long and short structures.
      A case of study - L59 region
AV (mag)




10’
We fitted the radial profile of each filament computing the radial distance
of each pixel from the identified spine. We mean the intensity of pixels with same
radial distance in region 4 pixels wide.

We find that quite a few filaments show a resolved radial profile with typical sizes
of ~50” (0.5 pc at 2.2 kpc)
                      Measure          Search for
Identify a sample
                    Morphological   correlations with
  of filaments in
                    and Physical     compact object
  unbiased way
                     properties        properties
                                                       Yellow:
                                                       Sources Detected
                                                       Inside filament

                                                       Red sources
                                                       Outside filaments



                                                       Adopting the catalog
                                                       of the compact objects
                                                       with detections at 3 bands


                                                      292 sources inside ~73     %

                                                      109 sources outside ~37        %
However, considering the sources that are detected only at SPIRE wavelengths
we find that the number of sources inside and outside filament are evenly splitted
                                             Still need to be investigated
                                        Virial Parameter
                                        Map


                                        M / Mvir

                                        Virial parameter
                                        from 13CO
                                        Galactic Ring
                                        Survey Data




Bright regions are supercritical –
correspond to the Cores/Clumps       See also Andrè talk
Fraction of Dense matter distributed in filamentary region:

All the observed higher density regions belong to filamentary regions

~50% of the matter with Av > 8 is identified as belonging to
a source inside the filament


                                Frea
Fraction of Dense matter distributed in filamentary region:

If we include all the sources detected only at SPIRE wavelengths
the fraction of dense matter found in sources inside the filaments
Increase.
                                                   There indication
                                                   that the number of sources
                                                   increase with the filament
                                                   Column Density

                                                   Need to be confirmed from
                                                   the analysis of more fields

                                 Denser Filament



Fraction          In Filaments     Out Filaments

With MIPS 24 μm 0.53±0.08          0.45±0.09
Conterpart
With PACS 70 μm 0.43±0.07          0.31±0.07
Conterpart
Sources with      0.58±0.13        0.56±0.20
Excess at 70 μm
                      Conclusions
Filaments are found everywhere in the Galaxy

They have a strong connection with the process of
Star Formation.

We developed methods to identify the regions
corresponding to the Filamentary structures and to
extract physical parameters from them (sizes, lengths,
masses)

First analysis on one field of the Hi-GAL survey
indicates that most of the dense matter is arranged in
the filaments. Moreover, compact objects
are found with more probability in those structures.

				
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posted:10/13/2012
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