Patrik Jonsson, UCSC

1
Simulations of dust in interacting galaxies

• Patrik Jonsson, UCSC
• In collaboration with
• TJ Cox, Joel Primack, Jennifer Lotz, Sandy Faber

2
Purpose

• Make realistic simulated observations of merger
  simulations
• Broadband images
• Spectral Energy Distributions
• Requires radiative transfer to take dust effects
  into account

3
Monte-Carlo method
Photons are emitted and scattered/absorbed
stochastically
4
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

5
To Date

• _at_ 20 merger scenarios completed
• _at_ 50 snapshots/scenario
• 11 viewpoints/snapshot
• 10 filters/viewpoint
• Many images
• 100,000 images, 10,000 SEDs
• Total of 1TB data

6
(No Transcript)
7
Sbc vs. G-series galaxies
G3G3b-u1
Sbc201a-u4
G-series has less gas and hence less star
formation and less dust.
(urz color)
8
With dust
Without dust
(urz color)
9
Integrated energy
UV/vis brightness practically constant
10
Magnitudes Colors
Rapid change of attenuation and color at
coalescence
11
All simulations
Mostly different orbital configurations
Looks good
12
CMD
13
Comparing to Heckman et al (98)
Explored correlations between quantities for
starbursts
Also Looks Pretty good
14
Real
Selection effect
15
But this is not so good correlation is in the
wrong direction!
16
The effect of mass
Dust direction
Mass direction
Fiducial
3 different Sbcs with different masses
17
The effect of IMF
Slope -3.3
Slope -2.35
Attenuation peaks at 60 instead of 80
18
The effect of orbit
Fiducial (prograde-prograde)
Retrograde-retrograde
RR is about 50 brighter, but only in IR
19
The effect of dust model
Milky-Way-type dust
SMC-type dust
20
Future

• Morphological analysis (Jennifer)
• SCUBA source comparison (Chapman)
• Improve SAM burst recipe
• What are we going to do with all the data?

21
The End
22
All viewpoints (long)
23
3 steps

• For every GADGET snapshot
• SED calculation
• Adaptive grid construction
• Radiative transfer

24
Adaptive grid
200kpc size with max resolution 2pc, equivalent
to a 1e53 uniform grid but with only 100k cells.
25
Adaptive Grid construction

• Start with uniform grid (103)
• Recursively subdivide cells into 23 subcells,
  until
• Maxlevel is reached
• Cell size lt min(r_i)fudge
• Recursively unify cells as long as
• (Sigma gas/ltgasgt lt gas tolerance AND
• Sigma L/ltLgt lt L tolerance) OR
• cell is uniform enough that lt 1 ray will be
  affected by unification

26
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

27
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

28
Radiative transfer stage

• Run entire SED at once without scattering
• Run with scattering for a single wavelength
• 106 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

29
IRX-Beta correlation