COMPLETE -...

Joao AlvesHéctor Arce

Chris Beaumont*Michelle Borkin*

Paola CaselliJames Di Francesco

Jonathan Foster*Alyssa Goodman (PI)Mark HeyerDoug Johnstone

Jens KauffmannHelen Kirk*

Di LiJaime Pineda*

Naomi RidgeErik Rosolowsky

Scott Schnee*Rahul ShettyMario TafallaAlyssa Goodman,

Harvard-Smithsonian Center for Astrophysics

Completely

COMPLETEup to Fall 2011

*=COMPLETE Ph.D.

+many thanks to special friends of COMPLETE: Hope Chen, Michael Halle, Marco Lombardi, Phil Myers, Stella Offner, Tom Robitaille, c2d Team, co-authors, undergrad interns...

Tuesday, September 20, 2011

http://www.worldwidetelescope.org/COMPLETE/WWTCoverageTool.htm


























Joao AlvesHéctor Arce

Chris Beaumont*Michelle Borkin*

Paola CaselliJames Di Francesco

Jonathan Foster*Alyssa Goodman (PI)Mark HeyerDoug Johnstone

Jens KauffmannHelen Kirk*

Di LiJaime Pineda*

Naomi RidgeErik Rosolowsky

Scott Schnee*Rahul ShettyMario TafallaAlyssa Goodman,

Harvard-Smithsonian Center for Astrophysics

Completely

COMPLETEup to Fall 2011

*=COMPLETE Ph.D.

Pineda

Caselli

Kauffmann

Goodman

+many thanks to special friends of COMPLETE: Hope Chen, Michael Halle, Marco Lombardi, Phil Myers, Stella Offner, Tom Robitaille, c2d Team, co-authors, undergrad interns...

Li




























Perseus

Ophiuchus

Serpens

The COordinated Molecular Probe Line Extinction Thermal Emission Survey of Star-Forming Regions


http://www.worldwidetelescope.org/COMPLETE/WWTCoverageTool.htm%23


























































Perseus

Ophiuchus

Serpens

www.cfa.harvard.edu/COMPLETE

The COordinated Molecular Probe Line Extinction Thermal Emission Survey of Star-Forming Regions

tinyurl.com/completepapers




























































http://www.cfa.harvard.edu/COMPLETE

http://www.cfa.harvard.edu/COMPLETE

http://tinyurl.com/completepapers

http://tinyurl.com/completepapers

The “COordinated Molecular Probe Line

Extinction Thermal Emission”

Survey of Star-Forming Regions


mm peak (Enoch et al. 2006)

sub-mm peak (Hatchellet al. 2005, Kirk et al. 2006)

13CO (Ridge et al. 2006)

mid-IR IRAC composite from c2d data (Foster, Laakso, Ridge, et al. in prep.)

Optical image (Barnard 1927)

Perseus


I will not do this to you...


YELLOW = something I didn’t know about or appreciate

before COMPLETE


20 minutes from now...

★ “Column Temperature”

★ 13CO poor tracer of column density, abundance not the problem

★ “lognormal” (but...)

★ “Cloudshine”

★ GNICEST (and CS!)

★ virial theorem over-used?

★ Dangers of p-p-v “observer” space

★ Perils of CLUMPFIND

★ Benefits of Dendrograms

★ Value of Tasting Dust & b-T

★ Spherical(!) Outflows

★ Cores in/out of Clusters NOT so Different

★ Coherent Cores are Real, and they Fragment (into filaments)!?

★ SLOW motion of cores & stars w.r.t. environs

★ Density “thresholds” are way more complicated than they look

★ Open Access is GOOD


20 minutes from now...

















Pineda

Kauffmann

Caselli

...and more later on from...

Heiderman


COMPLETE Perseus Column Density(Dust Emission , Extinction & Gas Emission)



Equivalent Av (mag)

2 4 6 8 10 120

5 pc

a)

Extinction (NICER/2MASS)30°

31°

32°

33°

Decli

natio

n (J

2000

)

Equivalent Av (mag)

0.15 0.20 0.25 0.30 0.35 0.40 0.45

b)

1- uncertainty (NICER)

Equivalent Av (mag)

2 4 6 8 10 120

c)

Dust Emission (IRAS)30°

31°

32°

33°

Decli

natio

n (J

2000

)

60 to 100 Color Temperature (K)

15 16 17 18 19 20 21 22

d)

Dust Color Temperature (IRAS)

Equivalent Av (mag)

2 4 6 8 10 120

e)

Line Emission (13CO)

03h50m0.00s 40m0.00s 30m0.00s

Right Ascension (J2000)

30°

31°

32°

33°

Decli

natio

n (J

2000

)

(13CO)

0.0 0.4 0.8 1.2 1.6 2.0

f)

Opacity (13CO)

03h50m0.00s 40m0.00s 30m0.00s


figures: Goodman, Pineda & Schnee 2009 cf. Schnee et al. 2005, 2006, 2008; Pineda et al. 2008



Equivalent Av (mag)

2 4 6 8 10 120

5 pc

a)


31°

32°

33°

Decli

natio

n (J

2000

)

Equivalent Av (mag)

0.15 0.20 0.25 0.30 0.35 0.40 0.45

b)


Equivalent Av (mag)

2 4 6 8 10 120

c)


31°

32°

33°

Decli

natio

n (J

2000

)


15 16 17 18 19 20 21 22

d)


Equivalent Av (mag)

2 4 6 8 10 120

e)


03h50m0.00s 40m0.00s 30m0.00s


30°

31°

32°

33°

Decli

natio

n (J

2000

)

(13CO)

0.0 0.4 0.8 1.2 1.6 2.0

f)

Opacity (13CO)

03h50m0.00s 40m0.00s 30m0.00s



0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(IRAS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(13

CO) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(2M

ASS)

(mag

))

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(IR

AS) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(13

CO) (

mag

))

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(13

CO) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(IR

AS) (

mag

))

2MASS/NICER

IRIS

13C

O13

CO

IRIS

2MASS/NICER


Column TemperatureAnd, the value of calibrating emission with extinction...

Schnee, Bethell & Goodman 2006


http://adsabs.harvard.edu/abs/2006ApJ...640L..47S

http://adsabs.harvard.edu/abs/2006ApJ...640L..47S


Equivalent Av (mag)

2 4 6 8 10 120

5 pc

a)


31°

32°

33°

Decli

natio

n (J

2000

)

Equivalent Av (mag)

0.15 0.20 0.25 0.30 0.35 0.40 0.45

b)


Equivalent Av (mag)

2 4 6 8 10 120

c)


31°

32°

33°

Decli

natio

n (J

2000

)


15 16 17 18 19 20 21 22

d)


Equivalent Av (mag)

2 4 6 8 10 120

e)


03h50m0.00s 40m0.00s 30m0.00s


30°

31°

32°

33°

Decli

natio

n (J

2000

)

(13CO)

0.0 0.4 0.8 1.2 1.6 2.0

f)

Opacity (13CO)

03h50m0.00s 40m0.00s 30m0.00s



0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(IRAS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(13

CO) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(2M

ASS)

(mag

))

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(IR

AS) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(13

CO) (

mag

))

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

A V(13

CO) (

mag

))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(

Equi

vale

nt A

V(IR

AS) (

mag

))

2MASS/NICER

IRIS

13C

O13

CO

IRIS

2MASS/NICER


Goodman, Pineda & Schnee 2009; Pineda et al. 2008


Yes, Column Density

Distribution is

“lognormal” (but...)

Goodman, Pineda & Schnee 2009; Pineda et al. 2008 cf. 2MASS results of Alves, Kainulainen, Lada, Lombardi et al.


...Justin Bieber, and the IMF, can be lognormal too...

see Beaumont et al. 2011, and http://www.ifa.hawaii.edu/users/beaumont/histograms/index.html

and so is any multiplicative random process.


http://www.ifa.hawaii.edu/users/beaumont/histograms/index.html

http://www.ifa.hawaii.edu/users/beaumont/histograms/index.html

A happy surprise.


A happy surprise.

Foster & Goodman 2006, Calar Alto JHK


Background: to appear in Foster, Mandel, et al. 2011Insets:

A happy surprise.

Foster & Goodman 2006, Calar Alto JHK


Extinction MappingNICE, NICER, NICEST, GNICER, GNICEST (and CS!)

Foster et al. 2008; Beaumont et al. 2011 (for “CS”)


Where and when does gravity matter?And, is the virial theorem over-used?

data, CLUMPFIND typically finds features on a limited range of scales,abovebut close to thephysical resolution of thedata, and its results canbe overly dependent on input parameters. By tuning CLUMPFIND’stwo free parameters, the same molecular-line data set8 can be used toshow either that the frequency distribution of clumpmass is the sameas the initial mass function of stars or that it follows the much shal-lower mass function associated with large-scale molecular clouds(Supplementary Fig. 1).

Four years before the advent of CLUMPFIND, ‘structure trees’9

were proposed as a way to characterize clouds’ hierarchical structure

using 2Dmaps of column density.With this early 2Dwork as inspira-tion, we have developed a structure-identification algorithm thatabstracts the hierarchical structure of a 3D (p–p–v) data cube intoan easily visualized representation called a ‘dendrogram’10. Althoughwell developed in other data-intensive fields11,12, it is curious that theapplication of treemethodologies so far in astrophysics has been rare,and almost exclusively within the area of galaxy evolution, where‘merger trees’ are being used with increasing frequency13.

Figure 3 and its legend explain the construction of dendrogramsschematically. The dendrogram quantifies how and where local max-ima of emission merge with each other, and its implementation isexplained in Supplementary Methods. Critically, the dendrogram isdetermined almost entirely by the data itself, and it has negligiblesensitivity to algorithm parameters. To make graphical presentationpossible on paper and 2D screens, we ‘flatten’ the dendrograms of 3Ddata (see Fig. 3 and its legend), by sorting their ‘branches’ to notcross, which eliminates dimensional information on the x axis whilepreserving all information about connectivity and hierarchy.Numbered ‘billiard ball’ labels in the figures let the reader matchfeatures between a 2D map (Fig. 1), an interactive 3D map (Fig. 2aonline) and a sorted dendrogram (Fig. 2c).

A dendrogramof a spectral-line data cube allows for the estimationof key physical properties associated with volumes bounded by iso-surfaces, such as radius (R), velocity dispersion (sv) and luminosity(L). The volumes can have any shape, and in other work14 we focus onthe significance of the especially elongated features seen in L1448(Fig. 2a). The luminosity is an approximate proxy for mass, suchthat Mlum5X13COL13CO, where X13CO5 8.03 1020 cm2K21 km21 s(ref. 15; see Supplementary Methods and Supplementary Fig. 2).The derived values for size, mass and velocity dispersion can then beused to estimate the role of self-gravity at each point in the hierarchy,via calculation of an ‘observed’ virial parameter, aobs5 5sv

2R/GMlum.In principle, extended portions of the tree (Fig. 2, yellow highlighting)where aobs, 2 (where gravitational energy is comparable to or largerthan kinetic energy) correspond to regions of p–p–v space where self-gravity is significant. As aobs only represents the ratio of kinetic energyto gravitational energy at one point in time, and does not explicitlycapture external over-pressure and/or magnetic fields16, its measuredvalue should only be used as a guide to the longevity (boundedness) ofany particular feature.

Self-gravitatingleaves

CLUMPFIND segmentation

vz

x (RA)

y (d

ec.)

vz

x (RA) y

(dec

.)

c

d

8

6

4

2

0

8

6

4

2

0

T mb

(K)

T mb

(K)

Self-gravitatingstructures

All structure

a b

Click to rotate

Figure 2 | Comparison of the ‘dendrogram’ and ‘CLUMPFIND’ feature-identification algorithms as applied to 13CO emission from the L1448region of Perseus. a, 3D visualization of the surfaces indicated by colours inthe dendrogram shown in c. Purple illustrates the smallest scale self-gravitating structures in the region corresponding to the leaves of thedendrogram; pink shows the smallest surfaces that contain distinct self-gravitating leaves within them; and green corresponds to the surface in thedata cube containing all the significant emission. Dendrogram branchescorresponding to self-gravitating objects have been highlighted in yellowover the range of Tmb (main-beam temperature) test-level values for whichthe virial parameter is less than 2. The x–y locations of the four ‘self-gravitating’ leaves labelled with billiard balls are the same as those shown inFig. 1. The 3D visualizations showposition–position–velocity (p–p–v) space.RA, right ascension; dec., declination. For comparison with the ability ofdendrograms (c) to track hierarchical structure, d shows a pseudo-dendrogram of the CLUMPFIND segmentation (b), with the same fourlabels used in Fig. 1 and in a. As ‘clumps’ are not allowed to belong to largerstructures, each pseudo-branch in d is simply a series of lines connecting themaximum emission value in each clump to the threshold value. A very largenumber of clumps appears in b because of the sensitivity of CLUMPFIND tonoise and small-scale structure in the data. In the online PDF version, the 3Dcubes (a and b) can be rotated to any orientation, and surfaces can be turnedon and off (interaction requires Adobe Acrobat version 7.0.8 or higher). Inthe printed version, the front face of each 3D cube (the ‘home’ view in theinteractive online version) corresponds exactly to the patch of sky shown inFig. 1, and velocity with respect to the Local Standard of Rest increases fromfront (20.5 km s21) to back (8 km s21).

Inte

nsity

leve

l

Local max

Local max

Local max

Merge

Merge

Leaf

Leaf

Leaf

Bra

nch

Trun

k

Test level

Figure 3 | Schematic illustration of the dendrogram process. Shown is theconstruction of a dendrogram from a hypothetical one-dimensionalemission profile (black). The dendrogram (blue) can be constructed by‘dropping’ a test constant emission level (purple) from above in tiny steps(exaggerated in size here, light lines) until all the local maxima and mergersare found, and connected as shown. The intersection of a test level with theemission is a set of points (for example the light purple dots) in onedimension, a planar curve in two dimensions, and an isosurface in threedimensions. The dendrogram of 3D data shown in Fig. 2c is the directanalogue of the tree shown here, only constructed from ‘isosurface’ ratherthan ‘point’ intersections. It has been sorted and flattened for representationon a flat page, as fully representing dendrograms for 3D data cubes wouldrequire four dimensions.

LETTERS NATURE |Vol 457 | 1 January 2009

64 Macmillan Publishers Limited. All rights reserved©2009

Goodman et al. 2009 Shetty et al. 2010

IS the virial theorem over-used?


mm peak (Enoch et al. 2006)

sub-mm peak (Hatchellet al. 2005, Kirk et al. 2006)

13CO (Ridge et al. 2006)

mid-IR IRAC composite from c2d data (Foster, Laakso, Ridge, et al. in prep.)

Optical image (Barnard 1927)

Perseus


“Keith” “Perseus”

Value of High-Dimensional Visualization and “Taste-Testing”...p-p-v space, and more...

“z” is depth into head “z” is line-of-sight velocity


AstronomicalMedicine@

Perseus


AstronomicalMedicine@

3D Viz made with VolView

Perseus


Where and when does gravity matter?And, is the virial theorem over-used?










vz

x (RA)

y (d

ec.)

vz

x (RA) y

(dec

.)

c

d

8

6

4

2

0

8

6

4

2

0

T mb

(K)

T mb

(K)


All structure

a b

Click to rotate


Inte

nsity

leve

l

Local max

Local max

Local max

Merge

Merge

Leaf

Leaf

Leaf

Bra

nch

Trun

k

Test level




Goodman et al. 2009 Shetty et al. 2010

IS the virial theorem over-used?


Perseus

“L1448+”


Dendrograms1.20

1.10

1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00

80 70 60 50 40 30 20

inte

nsity

leve

l

local max

local max

local max

merge

merge

Hierarchical “Segmentation”Rosolowsky, Pineda, Kauffmann & Goodman 2008


Dendrograms1.20

1.10

1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00

80 70 60 50 40 30 20

inte

nsity

leve

l

local max

local max

local max

merge

merge


Dendrograms1.20

1.10

1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00

80 70 60 50 40 30 20

inte

nsity

leve

l

local max

local max

local max

merge

merge

1-D: points; 2-D closed curves (contours); 3-D surfaces enclosing volumessee 2D demo at http://am.iic.harvard.edu/index.cgi/DendroStar/applet


http://am.iic.harvard.edu/index.cgi/DendroStar/applet

http://am.iic.harvard.edu/index.cgi/DendroStar/applet

What would CLUMPFIND do?1.20

1.10

1.00

0.90

0.80

0.70

0.60

0.50

0.40

0.30

0.20

0.10

0.00

80 70 60 50 40 30 20

inte

nsity

leve

l

local max

local max

local max

merge

merge

No hierarchy is allowed, all clumps go to the baseline.(Williams, De Geus & Blitz 1994)


from “The Perils of CLUMPFIND” by Pineda et al. 2009

CLUMPFIND



Look at what happens to the “CMF”!

CLUMPFIND



“Crowded” 3D data(very dangerous)


CLUMPFIND



“Crowded” 3D data(very dangerous)

“Sparse” 2D data(OK)


CLUMPFIND


Taste-Testing “Gravity”

Frac

tion

of E

mis

sion

in

Self-

Gra

vita

ting

Stru

ctur

es

Scale (pc)Taste Test

Gravity-free HD Simulations from Padoan et al. 2006; L1448 analysis from Rosolowsky et al. 2008, Goodman et al. 2009

both lines derived from 13CO “observations”










vz

x (RA)

y (d

ec.)

vz

x (RA)

y (d

ec.)

c

d

8

6

4

2

0

8

6

4

2

0

T mb

(K)

T mb

(K)


All structure

a b

Click to rotate


Inte

nsity

leve

l

Local max

Local max

Local max

Merge

Merge

Leaf

Leaf

Leaf

Bra

nch

Trun

k

Test level





Taste-Testing... Value of Tasting Dust & b-T

Shetty et al. 2009a, b; and see improved SED analysis method upcoming in Kelly et al. 2011(in prep.)


Taste-Testing... Value of Tasting Dust & b-T

Shetty et al. 2009a, b; and see improved SED analysis method upcoming in Kelly et al. 2011(in prep.)

small amounts of noise cause large problems (and spurious b-T

anticorrelation)


Arce et al. 2010, 2011

OutflowsBipolar & Spherical(!)


Momentum

0 300 600 900 1200 1500 1800 2100 2400 2700 3000

910

11

7

6

1

2

34

8

5

12

vexp= 5 km s-1vexp= 1 km s-1 B4 or earlier

B5 or laterIR-only

Total Bipolar Flows

Totals for“Shells”

Mass

0 100 200 300 400 500 600 700 800

Energy

0 10 20 30 40 50 60 70 80

50% of Cloud’s

Turbulent Energy

5 pc

lower confidence

Total Bipolar Flows

Total Bipolar Flows

[M]

[M km s-1]

[1045 erg]

Arce et al. 2011(in press, on astro-ph)



Momentum

0 300 600 900 1200 1500 1800 2100 2400 2700 3000

910

11

7

6

1

2

34

8

5

12

vexp= 5 km s-1vexp= 1 km s-1 B4 or earlier

B5 or laterIR-only

Total Bipolar Flows

Totals for“Shells”

Mass

0 100 200 300 400 500 600 700 800

Energy

0 10 20 30 40 50 60 70 80

50% of Cloud’s

Turbulent Energy

5 pc

lower confidence

Total Bipolar Flows

Total Bipolar Flows

[M]

[M km s-1]

[1045 erg]

Arce et al. 2011(in press, on astro-ph)


News FlashSpherical shells from young-ish stars may stir molecular clouds (much) MORE than bipolar flows, and B-stars may matter much.

Spherical(!) Outflows


Cores in and out of clustersNOT so different!

Foster, Rosolowsky, Kauffmann, Pineda, Borkin, Caselli, Myers & Goodman 2009; using NH3 survey of Rosolowsky et al. 2008


Cores in and out of clustersNOT so different!

Foster, Rosolowsky, Kauffmann, Pineda, Borkin, Caselli, Myers & Goodman 2009; using NH3 survey of Rosolowsky et al. 2008

(Once cores are “ungrouped”...)NOT so different!


The transition to coherence has been observed directly, thanks to GBT, EVLA, and Jaime Pineda--who will tell you all about this on Wednesday!

Coherent Cores are Real, and they Fragment (into filaments)!?

Pineda et al. 2010, 2011

(p-p-v structure of the) B5 region in Perseus


The transition to coherence has been observed directly, thanks to GBT, EVLA, and Jaime Pineda--who will tell you all about this on Wednesday!

Coherent Cores are Real, and they Fragment (into filaments)!?

Pineda et al. 2010, 2011


Jaime Pineda, Thursday


R.A.

Dec.

vLSR

many thanks to Jaime Pineda & Jens Kauffmann for this figureCOMPLETE data: 13CO from Ridge et al. 2006; NH3 from Pineda et al. 2010

weak 13COstrong 13CO

weak NH3 strong NH3



R.A.

Dec.

vLSR




weak NH3 strong NH3





weak NH3 strong NH3





weak NH3 strong NH3

SLOW Motion


Kirk et al. 2010

of cores with respect to cloudsof young stars with respect to cores

SLOW Motion


Kirk et al. 2010


1 km

s-1

SLOW Motion


Kirk et al. 2010

Is this as weird as it seems?

(See upcoming “Taste Test” by

Harris, Offner, Kirk, et al.)


1 km

s-1

SLOW Motion


Kirk et al. 2010


1 km

s-1

SLOW Motion


Jørgensen et al. 2008,

1 M pc-3

25 M pc-3

SCUBA coreYSO


SLOW Motion


Jørgensen et al. 2008,

1 M pc-3

25 M pc-3

SCUBA coreYSO

2 pc in 2 Myr

at 1 km s-1


SLOW Motion


Density and Column Density Thresholds for Star Formation

Kauffmann et al. 2010a,b,c...

Heiderman et al. 2010

Johnstone et al. 2004


Density and Column Density Thresholds for Star Formation

Kauffmann et al. 2010a,b,c...

Heiderman et al. 2010

Johnstone et al. 2004

KauffmannWay more complicated than they seem...

Heiderman


~900 citations, 10% to data paper...

H-index of 17, normalized H-index=

(17 papers)/(5 years since data paper)

=3.5!

Value of Open Access to Large Data SetsYes, I do mean you...

Note: I own “universe3d.org”--let me know if you’d like to contribute.


http://labs.adsabs.harvard.edu/ui/

Open Access is GOOD




YELLOW?(What I’ve learned...)


















YELLOW?(What I’ve learned...)

















Pineda

Kauffmann

Caselli

Tell me more...

Heiderman


What I’m (still) thinking about: B, g, accrete where you are?


FYI:Going Deeper...

Heiderman et al. 2010,Heiderman et al. 2010; cf. Pineda et al. 2008


FYI:Going Deeper...

Heiderman et al. 2010,

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(IRAS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(A V(13 CO

) (mag)

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(Eq

uivalen

t A V(2MAS

S) (ma

g))0.0 0.2 0.4 0.6 0.8 1.0 1.2

log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(A V(IR

AS) (m

ag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(Eq

uivalen

t A V(13 CO) (m

ag))

0.0 0.2 0.4 0.6 0.8 1.0 1.2log(AV(2MASS) (mag))

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(A V(13 CO

) (mag)

)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

log(Eq

uivalen

t A V(IRAS

) (mag)

)

Heiderman et al. 2010; cf. Pineda et al. 2008


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