patrik jonsson, ucsc in collaboration with tj cox, joel primack, jennifer lotz, sandy faber…

Patrik Jonsson, UCSCIn collaboration with

TJ Cox, Joel Primack, Jennifer Lotz, Sandy Faber…

Simulations of dust in interacting galaxies

Purpose

• Make realistic “simulated observations” of merger simulations:– Broadband images– Spectral Energy Distributions

• Requires radiative transfer to take dust effects into account

Monte-Carlo method“Photons” are emitted and scattered/absorbed stochastically

Outputs

• Data cube for each camera, typically 300x300 pixels x 500 wavelengths– Can be integrated to give images in broadband filters– Or look at spectral characteristics

• Absorbed energy in grid cells– Determines FIR luminosity reradiated by dust– Devriendt FIR template SED is added to integrated spectra

To Date:

• 20 merger scenarios completed

• 50 snapshots/scenario

• 11 viewpoints/snapshot

• 10 filters/viewpoint

=

• Many images… 100,000 images, 10,000 SEDs

Total of 1TB data

Sbc vs. G-series galaxiesG3G3b-u1 Sbc201a-u4

G-series has less gas and hence less star formation and less dust.

(urz color)

With dust

Without dust (urz color)

Integrated energy

UV/vis brightness practically constant

Magnitudes & Colors

Rapid change of attenuation andcolor at coalescence

All simulations

Looks good…

Mostly different orbital configurations

CMD

Also Looks Pretty good

Comparing to Heckman et al (98)Explored correlations between quantities for starbursts

Real

Selection effect

But this is not so good… correlation is in the wrong direction!

The effect of mass

Dust direction

Mass direction

3 different Sbcs with different massesFiducial

The effect of IMF

Slope -2.35 Slope -3.3

Attenuation peaks at 60% instead of 80%

The effect of orbit

Fiducial (prograde-prograde) Retrograde-retrograde

RR is about 50% brighter, but only in IR

The effect of dust model

Milky-Way-type dust SMC-type dust

Future

• Morphological analysis (Jennifer)

• SCUBA source comparison (Chapman)

• Improve SAM burst recipe

• …– What are we going to do with all the data?

The End

All viewpoints (long…)

3 steps

For every GADGET snapshot:

• SED calculation

• Adaptive grid construction

• Radiative transfer

Adaptive grid

200kpc size with max resolution 2pc, equivalent to a 1e5^3 uniform grid but with only 100k cells.

Adaptive Grid construction

• Start with uniform grid (10^3)

• Recursively subdivide cells into 2^3 subcells, until– Maxlevel is reached– Cell size < min(r_i)*fudge

• Recursively unify cells as long as– (Sigma gas/<gas> < gas tolerance AND– Sigma L/<L> < L tolerance) OR– “cell is uniform enough that < 1 ray will be affected by unification”

SED calculation

• Convolve SFR history with stellar model– Disk stars uniform SFR for 8 Gyr– Bulge stars instantaneous burst 8 Gyr old

• Single metallicity for SEDs

• Formed stars expand– 1km/s velocity dispersion

• End up with SED (500 points) for each particle

MC input parameters

• M_dust/M_gas– Effectively determines metallicity of gas at the start of the

simulation

• M_dust/M_metals– From metals produced during the simulation

• Dust model (Draine 03 MW)– Dust opacity, albedo and scattering characteristics

And the info from the grid, of course, luminosity and density of gas & metals in the cells

Radiative transfer stage

• Run entire SED at once without scattering

• Run with scattering for a single wavelength– 10^6 rays per wavelength, 11 view points– Repeat for 20 wavelengths between 20nm and 5um– And for lines (H alpha and H beta)

• Interpolate SED to full resolution

IRX-Beta correlation