Can we build a complete set of tools ? RT Can we build a complete - PowerPoint PPT Presentation

Can we build a complete set of tools ? RT

Can we build a complete set of tools ? RT Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Can we build a complete set of tools ? Chemistry, gas RT thermal balance Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Can we build a complete set of tools ? Chemistry, gas Model fitting : grid of RT models, MCMC thermal balance Fostino Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Can we build a complete set of tools ? Hydro models, dust evolution 
 Chemistry, gas Model fitting : grid of RT models, MCMC thermal balance Fostino Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Can we build a complete set of tools ? Hydro models, dust evolution 
 Chemistry, gas Model fitting : grid of RT models, MCMC thermal balance Fostino Feedback ? Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Can we build a complete set of tools ? Hydro models, dust evolution 
 Chemistry, gas Model fitting : grid of RT models, MCMC thermal balance Fostino Feedback ? Spectra, 
 Images Channel maps Interfero: nIR + mm (CASA) SEDs → Herschel, ALMA, → PIONIER/Gravity, JWST, SPICA ALMA, SPHERE/GPI

Example of post-processing Inclined binary with a disc: 
 1 million phantom SPH particules ➔ 1 million MCFOST Voronoi cells mcfost <para_file> -phantom <dump> Scattered light : 1.6μm Thermal emission : 1.3mm

Coupling hydro + chemistry + RT + 
 Astrochem Fargo model

Live coupling hydro + RT • mcfost is now available as a library (libmcfost.a) syntax specific to phantom (thanks Daniel) so far, 
 but trivial to extend to other code • pass SPH particles (position, velocity, n(a)) • MCFOST performs Voronoi tessellation + radiative transfer and returns T dust + radiation pressure vectors without interpolation • takes ~ few minutes for 10 6 particles : ‣ can be performed every few time steps yo get full hydro+RT simulations

MCFOST + phantom : 
 recovering hydrostatic equilibrium

MCFOST + phantom : 
 recovering hydrostatic equilibrium

Gas temperature mcfost + ProDiMo (Woitke 2009) model

Gas heating & cooling

Chemical abundances

Estimating Tgas via Machine Learning Prediction from 100 ProDiMo models training set Also predicts election density (-> MHD), molecular abundances

ALMA and SPHERE views of IM Lupi SPHERE H band DPI ~ 0.03” ALMA 1.3mm + 12CO 
 IM Lup 0.3” 0.1km/s 200 150 100 1" 1" 100au Avenhaus et al, in prep. 100 150 200 Pinte et al, 2017 i = 50 deg

12CO (2-1) 1" 35 25 Upper surface Upper surface 15 Lower surface Lower surface dv= − 1.28km/s dv= − 0.96km/s dv= − 0.64km/s 5 Tb [K] dv= − 0.32km/s dv= − 0.00km/s dv=0.32km/s dv=0.64km/s dv=0.96km/s dv=1.28km/s

13CO (2-1) 1" 20 15 10 Upper surface Upper surface Lower surface Lower surface 5 dv= − 1.28km/s dv= − 0.96km/s dv= − 0.64km/s Tb [K] dv= − 0.32km/s dv= − 0.00km/s dv=0.32km/s dv=0.64km/s dv=0.96km/s dv=1.28km/s

1" 35 25 Upper surface Upper surface 15 Lower surface Lower surface dv= − 1.28km/s dv= − 0.96km/s dv= − 0.64km/s 5 Upper surface Tb [K] Far side T op of the CO layer dv= − 0.32km/s dv= − 0.00km/s dv=0.32km/s observed in 12 CO Bottom of the CO layer observed in 13 CO and C 18 O dv=0.64km/s dv=0.96km/s dv=1.28km/s gas CO layer Lower surface Far side Upper surface Near side vertical snow line Lower surface Bottom of the CO layer Near side observed in 12 CO atomic and ionized layer

Reconstructing the altitude, velocity and temperature of the CO emitting layers r ◆ 2 ✓ y f − y c F N ( x − x ? ) 2 + r = . Flux cos i Upper surface far side The altitude h of the orbit h = y c − y ? Position along vertical axis sin i . F (x, y f ) emis- y b − y c (x c , y c ) r h sin i (x , y ) as 3 = ( 3 obs − 3 syst ) ( x − x ? ) sin i . Upper surface near side N (x, y n ) T b = T ex (1 − e − τ ) Lower surface near side Tex ≈ Tgas for 
 δ x 1" ∆ v = 0.80 km/s low J CO lines

The CO layers 150 12 CO 13 CO C 18 O 100 h CO [au] 50 100 200 300 400 r [au]

Vertical velocity gradient & sub-Keplerian rotation v Kep (r,z=0) 4 v Kep (r,z) 3 2 GM ? r 1 @ P v(r,z), with dP/dr r = ( r 2 + h 2 ) 3 / 2 + @ r . ⇢ gas v [km.s − 1 ] 3 2 1 100 200 300 400 r [au]

Mapping the vertical 
 snow line 12 CO upper surface Flux dilution 30 max(T b ) [K] T = 21K 20 Upper surface 12 CO lower surface Far side 13 CO upper surface T op of the CO layer observed in 12 CO Bottom of the Flux CO layer observed in 13 CO and C 18 O dilution gas CO layer 10 Lower surface C 18 O upper surface Far side Upper surface Near side vertical snow line Lower surface Bottom of 100 200 300 400 the CO layer Near side observed in 12 CO r [au] atomic and ionized layer

Comparison with models 0.5 50 45 0.4 40 35 0.3 30 h/r T [K] 25 0.2 20 15 0.1 10 5 100 200 300 400 500 r [au]

CO layers vs scattered light layer 150 12 CO 13 CO C 18 O 100 h CO [au] 50 IM Lup 100 200 300 400 r [au] Avenhaus et al, in prep.

Concluding remarks • New ALMA and adaptive optics observations require advanced models coupling hydro + RT + chemistry • Modern continuum RT codes can be coupled efficiently with hydro codes • T gas, ionisation chemistry can be estimated via Machine Learning trained on databases of thermo-chemical models