Filaments in the Galaxy: Their propertiesand 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

MIPS 24 μm

Outline

MIPS 24 μm

SPIRE 250 μm

MIPS 24 μm

SPIRE 250 μm

Pre-Herschel

For Example:

Taurus Complex in 13CO J = 1 - 0

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) 5 pc

3 parallel filaments plus other coming out from the “hub” L1495

L1495

L1508

L1536

L1521

Observations in optical, near-IR, submm and in CO rotational linesshow 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 foundalong 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

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

120’

Highly structured Emission

Difficulties in theextraction of compact objects

Qualitatively the sources cluster on the filaments.

L 59 field

SPIRE 250 μm map

Highly structured Emission

Difficulties in theextraction of compact objects

With the aid of an high pass band filter the emissionIs damped.

Dense compactstructures start tostick out!

Image credit: Molinari 2010

Complex network

of substructures

Candidate source

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)

Identify a sample of

filaments in unbiased way

Identify a sample of

filaments in unbiased way

Measure Morphological and Physical properties

Measure Morphological and Physical properties

Search for correlations with compact object

properties

Search for correlations with compact object

properties

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 basisof 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 basisof 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 basisof 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

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)

Filament identification in a nutshell - 1

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 thresholdvalue 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 SpanningTree (MST) that give the unique path linking together all the pixels of the spine.

Filament identification in a nutshell - 2

We compute the eigenvalues of the Hessian Matrix and determine a thresholdvalue 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)

Input Image

Simulation Filament with 2 sources Contrast Filament – Background ~ 9

Eigenvalues of the Hessian Matrix

Thresholded mask Application Morphological Operator

Filament Simulations

Fluctuation on the Spine

We build up a simple simulation ofof 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 spineIntroduced 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) simulating the emission of the diffuse ISM

Filament Simulations

Fluctuation on the Spine

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

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

Identify a sample of

filaments in unbiased way

Identify a sample of

filaments in unbiased way

Measure Morphological and Physical properties

Measure Morphological and Physical properties

Search for correlations with compact object

properties

Search for correlations with compact object

properties

A case of study - L59 region

10’

MIPS 24 μmVulpecula region

NGC 6823

See Billot et al. 2010, Elia et al.2010

Well know filamentaryregions.

Vulpecula OB1 association.

A case of study - L59 region

10’

PACS 160 μm SPIRE 250 μm

SPIRE 250 μm

10’

Using the photometry package CuTEx (Molinari et al. 2010) we identified the compactsources in the Vulpecula field. Improvement in the extraction techniques allowed us tofind 401 candidates with robust indipendent detection in the 3 consecutive bands of 160, 250, 350 μm.

A case of study - L59 region

SPIRE 250 μm

10’

Source detectedBy CuTEx in at least3 phtoometryc band

A case of study - L59 region

SPIRE 250 μm

10’

10’

AV (mag)We computed the ColumnDensity and Temperature maps fitting modified graybody functions by pixel by pixel.

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

A case of study - L59 region

Temperature Map

A case of study - L59 region

10’

AV (mag)We identied 50 filamentsthat 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 scalestructures, highlighted by the derivative computatio.n

Identify a sample of

filaments in unbiased way

Identify a sample of

filaments in unbiased way

Measure Morphological and Physical properties

Measure Morphological and Physical properties

Search for correlations with compact object

properties

Search for correlations with compact object

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

10’

AV (mag)

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)

Identify a sample of

filaments in unbiased way

Identify a sample of

filaments in unbiased way

Measure Morphological and Physical properties

Measure Morphological and Physical properties

Search for correlations with compact object

properties

Search for correlations with compact object

properties

Yellow:Sources Detected Inside filament

Red sources Outside filaments

Adopting the catalog of the compact objectswith detections at 3 bands

292 sources inside ~73 %

109 sources outside ~37 %

However, considering the sources that are detected only at SPIRE wavelengthswe find that the number of sources inside and outside filament are evenly splitted

Still need to be investigated

Bright regions are supercritical – correspond to the Cores/Clumps

Virial ParameterMap

M / Mvir

Virial parameterfrom 13CO Galactic Ring Survey Data

See also Andrè talk

FreaFrea

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

Fraction of Dense matter distributed in filamentary region:

If we include all the sources detected only at SPIRE wavelengthsthe fraction of dense matter found in sources inside the filamentsIncrease.

Fraction In Filaments Out Filaments

With MIPS 24 μm Conterpart

0.53±0.08 0.45±0.09

With PACS 70 μmConterpart

0.43±0.07 0.31±0.07

Sources withExcess at 70 μm

0.58±0.13 0.56±0.20

There indicationthat the number of sources increase with the filamentColumn Density

Need to be confirmed from the analysis of more fields

Denser Filament

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.