John E. HibbardNorth American ALMA

Science Center (NAASC/NRAO)

Josh BarnesInstitute for Astronomy

U. Hawai’i

(simulating the dynamics of the…)

Gas in Interacting Galaxies

“Gas & Stars in Galaxies: A Multi-wavelength 3D perspective” ESO, Garching, June 10-13 2008

Peculiar Galaxies: dynamically unrelaxed (non-equilibrium) forms

Toomre Sequence of On-going Mergers (Toomre 1977) from Arp Atlas of Peculiar Galaxies (Arp 1966)

5%-10% of population in local universe

In UGC, ~600 out of 9000 galaxies (~7%) with morphological descriptions including: disrupted, distorted, disturbed, interacting, eruptive, peculiar, bridge, loop, plume, tail, jet, streamer, connected (note, some are multiple systems, but not all need be interacting)

Total fraction that went through a peculiar phase = %peculiar * T/tpeculiar

Morphologies (& Kinematics!) can be explained by galaxy-galaxy

interactions

Seminal Paper (1369 citations): Toomre & Toomre 1972

Neutral Hydrogen in Galaxies

B/W=optical image of NGC 6946 from Digital Sky Survey

Blue=Westerbork Synthesis Radio Telescope 21 cm image of Neutral Hydrogen (Boomsma 2007 PhD Thesis)

Neutral Hydrogen is the raw fuel for all star formation

Hydrogen usually much more extended than stars

Dynamically cold & extended HI responds strongly to the tidal

forces M81/M82/NGC3077VLA 12-pointing mosaic

Yun et al. 1994

HI contours on DSS: van der Hulst, 1977, PhD. Thesis

HI kinematics strongly affirmed interaction hypothesis

Spectral Line Maps are inherently 3-dimensional

For illustrations, You must choose between many 2-dimensional projections

1-D Slices along velocity axis = line profiles

2-D Slices along velocity axis = channel maps

Slices along spatial dimension = position velocity profiles

Integration along the velocity axis = moment maps

“Channel Maps”spatial distribution of line flux at each successive

velocity setting

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Emission from channel maps contoured upon an optical image

Moment Maps

Zeroth MomentIntegrated flux

First Momentmean velocity

Second Momentvelocity dispersion

Position-Velocity Profiles

Slice or Sum the line emission over one of the two spatial dimensions, and plot against the remaining spatial dimension and velocity

Susceptible to projection effects

+250 km/s-250 km/s

-250 km/s

+250 km/s

Rotating datacubes gives complete picture of data, noise, and remaining systematic

effects

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QuickTime™ and aYUV420 codec decompressor

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Karma “xray” package & Oosterloo “cube2mpeg”

Rotations emphasize kinematic continuity and help separate out projection effects

3-D rendering program “xray” in the Karma visualization package & “cube2mpeg”http://www.atnf.csiro.au/computing/software/visualisation/http://www.atnf.csiro.au/computing/software/karma/Gooch, R.E., 1996, "Karma: a Visualisation Test-Bed", in Astronomical Data Analysis Software and Systems V, ASP Conf. Series vol. 101, ed. G.H. Jacoby & J. Barnes, ASP, San Francisco, p.80-83, ISSN 1080-7926

Rotations emphasize kinematic continuity and help separate out projection effects

QuickTime™ and aYUV420 codec decompressor

are needed to see this picture.

3-D rendering program “xray” in the Karma visualization package & “cube2mpeg”

http://www.atnf.csiro.au/computing/software/visualisation/

http://www.atnf.csiro.au/computing/software/karma/

Gooch, R.E., 1996, "Karma: a Visualisation Test-Bed", in Astronomical Data Analysis Software and Systems V, ASP Conf. Series vol. 101, ed. G.H. Jacoby & J. Barnes, ASP, San Francisco, p.80-83, ISSN 1080-7926

3rd dimension allows us to construct more accurate numerical models

“Identikit” Mk 0

Hibbard 1995

A few dozen model “matches” to interacting galaxies have been published

Only a handful “match” spatially resolved kinematics

Models of binary interactions have a large

parameter space

Model Matching: the Hard Way

Build two model galaxies (B+D+H; Barnes 1988; Barnes & Hernquist 1996)

Select 1 encounter geometry; run

Model Matching: the Hard Way

Run model Match Build another & run Compare; decide how

to change params Etc… Takes ~50 trials to get

decent fit to simple forms (N7252 Hibbard & Mihos 1995; N4676 Hibbard & Barnes 1997)

Identikit Mk 0.5: 9x18x2=324 simulations

in one

Identikit Mk 1: simulate all disk geometries

Populate live halo with swarm of test particles on circular orbits

Display only test particles with initial angular momentum closely aligned with desired disks

Test: generate 36 random BDH simulation & match

generate 36 random BDH self-consistent N-body simulations

Read into Identikit & Match

Subjectively grade fit: good, fair, poor

Check fit vs. actual parameters

Barnes & Hibbard 2008 submitted

Identikit interactive modeling tool

Identikit interactive modeling tool

Red=“good” fits

Black=“fair” fits

Cyan=“poor” fits

Disk orientation parameters

Viewing Angles

Disk orientation All fits: 25deg Good fits: 10deg

Viewing angles All fits: 25deg Good fits: 8deg

Red=“good” fits

Black=“fair” fits

Cyan=“poor” fits

Time since pericenter

Pericentric Separation

Time since pericenter

All fits: 14% Good fits: 9%

Pericentric separation

All fits: 25% Good fits: 15%

Red=“good” fits

Black=“fair” fits

Cyan=“poor” fits

Linear Scale Factor

Velocity Scale Factor

Linear scale factor All fits: 16% Good fits: 10%

Velocity scale factor*

All fits: 15% Good fits: 5%

Identikit interactive modeling tool

Can match models fairly well Models judged as good fits are better

able to recover true parameters Perhaps more importantly, models that

do not recover true parameters are judged as fair/poor fits (no false positives)

Caveat: simulated “real” systems had the same radial mass profile as Identikit models

Tools like Identikit 1 can greatly speed model matching process

Hibbard, 1993-1997: Identikit 0 for N7252, N4676, N4038 ~50 simulations per system, ~2mo

each Barnes, 2008: Identikit 1:

matched 36 systems in ~1 mo

Why bother matching?

So you know some angles and scale factors, so what?

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Time Evolution

System made first pass ~220 Myr ago; will merge in ~40 Myr

1 million particlesimulation of best fitting parameters

http://www.ifa.hawaii.edu/~barnes/pressrel/antfacts/

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3-dimensional structure of The Antennae

http://www.ifa.hawaii.edu/~barnes/pressrel/antfacts/

Why is base of northern tail devoid of HI, while southern tail is gas rich?

- Gas in northern tail has direct view of young SSCs with ionizing radiation- Gas in southern tail does not

Simulation confirms that 3-D geometry is suitable for this interpretation

Next Generations telescopes require novel visualization

approaches 2009: EVLA widar correlator

2:1 bandwidth ratios16k-4M channels!

2012: ALMA10 bands4096 channels per IFHundreds of atomic & molecular transitions in the

mm-submm IN THE SAME DATA CUBES

Both: All spectral mode, all the time

Imaging Chemistry in Galaxies

IC 342 — Owens Valley Millimeter Array

PC Axis 1:

Density-weightedmean column density

PC Axis 2:

Shock tracers vs PDR molecules

Gas density: CO, N2H+, HCNC2H: PDR

Methanol, HCNO: shocks

Meier & Turner 2005

Detected interstellar molecules

H2 HD H3+ H2D+ CH CH+ C2 CH2 C2H *C3

CH3 C2H2 C3H(lin) c-C3H *CH4 C4

c-C3H2 H2CCC(lin) C4H *C5 *C2H4 C5HH2C4(lin) *HC4H CH3C2H C6H *HC6H H2C6

*C7H CH3C4H C8H *C6H6

OH CO CO+ H2O HCO HCO+HOC+ C2O CO2 H3O+ HOCO+ H2COC3O CH2CO HCOOH H2COH+ CH3OH CH2CHOCH2CHOH CH2CHCHO HC2CHO C5O CH3CHO c-C2H4O CH3OCHO CH2OHCHO CH3COOH CH3OCH3 CH3CH2OH CH3CH2CHO(CH3)2CO HOCH2CH2OH C2H5OCH3 (CH2OH)2CONH CN N2 NH2 HCN HNC N2H+ NH3 HCNH+ H2CN HCCN C3NCH2CN CH2NH HC2CN HC2NC NH2CN C3NHCH3CN CH3NC HC3NH+ *HC4N C5N CH3NH2

CH2CHCN HC5N CH3C3N CH3CH2CN HC7N CH3C5N? HC9N HC11NNO HNO N2O HNCO NH2CHO SH CS SO SO+ NS SiH*SiC SiN SiO SiS HCl *NaCl*AlCl *KCl HF *AlF *CP PNH2S C2S SO2 OCS HCS+ c-SiC2

*SiCN *SiNC *NaCN *MgCN *MgNC *AlNCH2CS HNCS C3S c-SiC3 *SiH4 *SiC4

CH3SH C5S FeO

DEMIRM

END

Young star clusters

Why is base of northern tail devoid of HI, while southern tail is gas rich?

- gas in northern tail has direct view of young SSCs with ionizing radiation

- Gas in southern tail does notSimulation confirms that 3-D geometry is suitable for this interpretation