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Introduction IM Lup Gas & Dust Herschel Summary
Multi-technique observations and modelling
of protoplanetary disks
Christophe Pinte
University of Exeter
Christophe Pinte Multi-technique modelling 1/12
Introduction IM Lup Gas & Dust Herschel Summary
Collaborators
F. Menard, G. Duchene, J.C. Augereau,J. Olofsson, C. Ceccarelli, G. Duvert (LAOG,Grenoble)
D.L. Padgett, K. Stapelfeldt (IPAC &
JPL/Caltech)
G. Schneider (Steward Observatory)
P. Woitke, W.F. Thi, I. Tilling (ROE,Edinburgh)
I. Kamp (KAI, Groningen)
J. Cernicharo (DAMIR/CSIC, Madrid)
the GASPS teamChristophe Pinte Multi-technique modelling 2/12
Introduction IM Lup Gas & Dust Herschel Summary
Protoplanetary disks
Gas and dust disks are the birthplace of planets
?
Goal : characterizing the first steps of planet formation
Understanding disk structure as well as dust grain and gasproperties to test their evolution processes. For instance:
grain growth and settling
gas dissipation
formation of a large variety of planetary systems
Christophe Pinte Multi-technique modelling 3/12
Introduction IM Lup Gas & Dust Herschel Summary
A variety of observations
Each approach probes adifferent part of the disk
Disk modelling mustconsider as manyobservations as possibleat once
Multi-λ modelling
Not so frequent. . . but necessary!
HST/Nicmos HST/WFPC2 HST/WFPC21.6 µm 0.8 µm 0.6 µm
SMA 1.3mmVLTI Spitzer 5-30 µm
Christophe Pinte Multi-technique modelling 4/12
Introduction IM Lup Gas & Dust Herschel Summary
A variety of observations
Each approach probes adifferent part of the disk
Disk modelling mustconsider as manyobservations as possibleat once
Multi-λ modelling
Not so frequent. . . but necessary!
CO
OI CII
H2O
rotational lines
The same is true for the gas phase
Christophe Pinte Multi-technique modelling 4/12
Introduction IM Lup Gas & Dust Herschel Summary
Multi-λ modelling of IM Lupi Pinte et al, 2008
RT modelling with MCFOST (Pinte et al 2006)
A single model with mild stratification remarkably reproduces allobservations: H(1 mm)≈ 0.5 - 0.75 H(1 µm)
1.0 10.0 100.0 1000.
10−16
10−14
10−12
10.0 20.5.
10−13
2
λ (µm)
λ.F
λ (W
.m−
2 )
⇓
Mdisk, grain growthand settling
1’’N
EWFPC2 0.6 µm
Nicmos 1.6 µmNicmos 1.6 µm
Obs Models
⇓
Rext, i , H0, grain sizes& composition
0.1
0.2
0.05
0 50 100
0.01
0.02
0.005
Fν
(Jy)
Baseline (kλ)
Fν
(Jy)
SMA 1.3mm
ATCA 3.3mm
p=1
p=1
p=2
p=2
p=0
p=0
⇓
Surface densityΣ(r) ∝ r−p
Christophe Pinte Multi-technique modelling 5/12
Introduction IM Lup Gas & Dust Herschel Summary
IM Lup : quantitative constraints Pinte et al, 2008
Bayesian probabilities
SED + . . .
≈ 400 000 models
Some parameters fixedfrom data (amax,Mdust, Rout)+ Fitting 6 parameters
Bayesian approach toestimate modelparameters
0.00.51.00.0
0.5
1.0
−2−1 00.0
0.5
1.0
1.0 1.1 1.20.0
0.5
1.0
0.1 1.00.20.05 0.50.0
0.2
0.4
0.6
5 10 150.0
0.5
1.0
0.00 0.05 0.10 0.15 0.200.0
0.2
0.4
0.6
cos(i)cos(i)cos(i)cos(i)cos(i) surface densitysurface densitysurface densitysurface densitysurface density flaringflaringflaringflaringflaring
rin (AU)rin (AU)rin (AU)rin (AU)rin (AU) h0 (AU)h0 (AU)h0 (AU)h0 (AU)h0 (AU) stratificationstratificationstratificationstratificationstratification
Pro
bability
Pro
bability
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bability
Pro
bability
Pro
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Pro
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Pro
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Pro
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Pro
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Pro
bability
Christophe Pinte Multi-technique modelling 6/12
Introduction IM Lup Gas & Dust Herschel Summary
IM Lup : quantitative constraints Pinte et al, 2008
Bayesian probabilities
SED + mm visibilities + . . .
≈ 400 000 models
Some parameters fixedfrom data (amax,Mdust, Rout)+ Fitting 6 parameters
Bayesian approach toestimate modelparameters
0.00.51.00.0
0.5
1.0
−2−1 00.0
0.5
1.0
1.0 1.1 1.20.0
0.5
1.0
0.1 1.00.20.05 0.50.0
0.2
0.4
0.6
5 10 150.0
0.5
1.0
0.00 0.05 0.10 0.15 0.200.0
0.2
0.4
0.6
cos(i)cos(i)cos(i)cos(i)cos(i) surface densitysurface densitysurface densitysurface densitysurface density flaringflaringflaringflaringflaring
rin (AU)rin (AU)rin (AU)rin (AU)rin (AU) h0 (AU)h0 (AU)h0 (AU)h0 (AU)h0 (AU) stratificationstratificationstratificationstratificationstratification
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Christophe Pinte Multi-technique modelling 6/12
Introduction IM Lup Gas & Dust Herschel Summary
IM Lup : quantitative constraints Pinte et al, 2008
Bayesian probabilities
SED + mm visibilities + scattered light images
≈ 400 000 models
Some parameters fixedfrom data (amax,Mdust, Rout)+ Fitting 6 parameters
Bayesian approach toestimate modelparameters
0.00.51.00.0
0.5
1.0
−2−1 00.0
0.5
1.0
1.0 1.1 1.20.0
0.5
1.0
0.1 1.00.20.05 0.50.0
0.2
0.4
0.6
5 10 150.0
0.5
1.0
0.00 0.05 0.10 0.15 0.200.0
0.2
0.4
0.6
cos(i)cos(i)cos(i)cos(i)cos(i) surface densitysurface densitysurface densitysurface densitysurface density flaringflaringflaringflaringflaring
rin (AU)rin (AU)rin (AU)rin (AU)rin (AU) h0 (AU)h0 (AU)h0 (AU)h0 (AU)h0 (AU) stratificationstratificationstratificationstratificationstratification
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Christophe Pinte Multi-technique modelling 6/12
Introduction IM Lup Gas & Dust Herschel Summary
IM Lup : quantitative constraints Pinte et al, 2008
Bayesian probabilities
SED + mm visibilities + scattered light images
≈ 400 000 models
Some parameters fixedfrom data (amax,Mdust, Rout)+ Fitting 6 parameters
Bayesian approach toestimate modelparameters
0.00.51.00.0
0.5
1.0
−2−1 00.0
0.5
1.0
1.0 1.1 1.20.0
0.5
1.0
0.1 1.00.20.05 0.50.0
0.2
0.4
0.6
5 10 150.0
0.5
1.0
0.00 0.05 0.10 0.15 0.200.0
0.2
0.4
0.6
cos(i)cos(i)cos(i)cos(i)cos(i) surface densitysurface densitysurface densitysurface densitysurface density flaringflaringflaringflaringflaring
rin (AU)rin (AU)rin (AU)rin (AU)rin (AU) h0 (AU)h0 (AU)h0 (AU)h0 (AU)h0 (AU) stratificationstratificationstratificationstratificationstratification
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Pro
bability
Christophe Pinte Multi-technique modelling 6/12
Introduction IM Lup Gas & Dust Herschel Summary
Simultaneous modelling of gas & dust
Detailed model of the dust disk ofIRAS 04158+2805Glauser et al, 2008
Radiative transfer + chemistry
1 Temperature structure, UV fluxfrom MCFOST
2 CO abundance from chemistrymodel (coll. with C. Ceccarelli)
3 Level populations and emissionmaps with MCFOST
⇒ central mass (0.3-0.5M⊙) → ageturbulence (0.2-0.3 km.s−1)
Obs Model
−4 −2 0 2 4
0
5
10
−4 −2 0 2 4
v (km.s−1)F
lux
(Jy)
v (km.s−1)
Scattered light (HST) &12CO(3-2) (SMA)
Christophe Pinte Multi-technique modelling 7/12
Introduction IM Lup Gas & Dust Herschel Summary
GASPS and the DENT grid of models
Gas in Protoplanetary Systems (GASPS) key program
Systematic survey of gas and dust in disks (PI: W. Dent)
≈ 250 disks, 0.3 to 30 Myrs, spectral types: M4 to B2
PACS: OI, CII, CO and H2O + continuum
The DENT model grid (see poster B47 by P. Woitke)
coupling MCFOST & ProDiMo (Woitke et al 2009)
SEDs + NLTE line fluxes (+physical & chemicalstructure) for ≈ 320 000 disks models
⇒ dependence of continuum & line observation on star, diskand dust properties
Christophe Pinte Multi-technique modelling 8/12
Introduction IM Lup Gas & Dust Herschel Summary
Signatures of dust settling
Far-IR
is the regime where signatures ofdust settling are maximum⇒ probed by Herschel
Color-magnitude diagrams
distinction between settledand non settled disks
independent of the geometry,dust properties, . . . if Teff andMdisk known
Spitzer
Herschel
IRAM
Christophe Pinte Multi-technique modelling 9/12
Introduction IM Lup Gas & Dust Herschel Summary
Signatures of dust settling
Far-IR
is the regime where signatures ofdust settling are maximum⇒ probed by Herschel
Color-magnitude diagrams
distinction between settledand non settled disks
independent of the geometry,dust properties, . . . if Teff andMdisk known
Spitzer
Herschel
IRAM
31.5 32.0 32.5 33.0 33.5
2
3
4
[24 µm]
[210
µm
]-[2
4µm
]
Christophe Pinte Multi-technique modelling 9/12
Introduction IM Lup Gas & Dust Herschel Summary
Constraining the gas disk
Herschel/PACS
should detect lines even for lowluminosity T Tauri stars
First results of the DENT grid
Most lines scale withstellar parameters: Lstar
and UV excess
Line ratios eliminate themain scalings→ information about diskmass and shape
GASPS sensitivity limit
Christophe Pinte Multi-technique modelling 10/12
Introduction IM Lup Gas & Dust Herschel Summary
H2O lines with HIFI Cernicharo et al, 2009
Dust settling
dramatically changes theconditions for water vapor indisk⇒ H2O lines powerful tool
But dangerous
strongly depends oncollisional coefficients
Christophe Pinte Multi-technique modelling 11/12
Introduction IM Lup Gas & Dust Herschel Summary
Concluding remarks
A variety of datasets = finer disk models
Dust evolution and spatial differentiation are frequent indisks around T Tauri stars
By combining line and continuum data, more detailedinformation about the disks
Testing the physics of dust grains towards planet formation
Grain growth
Separate dust populations
dust and gas evolution?
Simultaneous studies of the gas & dust phases
are promising in the context of Herschel and ALMA
Christophe Pinte Multi-technique modelling 12/12