John E. Hibbard North American ALMA Science Center (NAASC/NRAO) Josh Barnes Institute 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

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/  t peculiar

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/NGC3077 VLA 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

Emission from channel maps contoured upon an optical image

Moment Maps Zeroth Moment Integrated flux First Moment mean velocity Second Moment velocity 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

Rotating datacubes gives complete picture of data, noise, and remaining systematic effects 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” software/visualisation/ software/karma/ Gooch, R.E., 1996, "Karma: a Visualisation Test-Bed"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

Rotations emphasize kinematic continuity and help separate out projection effects  3-D rendering program “xray” in the Karma visualization package & “cube2mpeg”  omputing/software/visuali sation/  omputing/software/karma / omputing/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 , ISSN Gooch, R.E., 1996, "Karma: a Visualisation Test-Bed"

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)

HI: Hibbard & van Gorkom 1996; CO: Yun & Hibbard 2001; H  : Mihos et al Identikit Mk 0: 1 simulation per disk geometry

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

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, :  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?

Time Evolution System made first pass ~220 Myr ago; will merge in ~40 Myr 1 million particle simulation of best fitting parameters

3-dimensional structure of The Antennae

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

HI displacement could be due to ionization or RPS by wind fluid

NGC7252: HI streaming in from tidal tails  Tails must extend back into remnant, but HI ends abruptly  Gas is currently falling back into remnant at 1- 2 Mo/yr  Tails must extend back into remnant, but HI ends abruptly  Gas is currently falling back into remnant at 1- 2 Mo/yr  Yet body remains devoid of HI

N-body simulation of NGC 4676 “The Mice” Barnes 2004 MNRAS; Hibbard & Barnes, in preparation Comparison with observations provides check on model prescriptions

N-body simulation of NGC 4676 “The Mice” Barnes 2004 MNRAS; Hibbard & Barnes, in preparation Comparison with observations provides check on model prescriptions

Next Generations telescopes require novel visualization approaches  2009: EVLA widar correlator  2:1 bandwidth ratios  16k-4M channels!  2012: ALMA  10 bands  4096 channels per IF  Hundreds 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- weighted mean column density PC Axis 2: Shock tracers vs PDR molecules Gas density: CO, N 2 H +, HCN C 2 H: PDR Methanol, HCNO: shocks Meier & Turner 2005

Detected interstellar molecules H 2 HD H 3 + H 2 D+ CH CH + C 2 CH 2 C 2 H *C 3 CH 3 C 2 H 2 C 3 H(lin) c-C 3 H *CH 4 C 4 c-C 3 H 2 H 2 CCC(lin) C 4 H *C 5 *C 2 H 4 C 5 H H 2 C 4 (lin) *HC 4 H CH 3 C 2 H C 6 H *HC 6 H H 2 C 6 *C 7 H CH 3 C 4 H C 8 H *C 6 H 6 OH CO CO+ H 2 O HCO HCO+ HOC+ C 2 O CO 2 H 3 O+ HOCO+ H 2 CO C 3 O CH 2 CO HCOOH H 2 COH+ CH 3 OH CH 2 CHO CH 2 CHOH CH 2 CHCHO HC 2 CHO C 5 O CH 3 CHO c-C 2 H 4 O CH 3 OCHO CH 2 OHCHO CH 3 COOH CH 3 OCH 3 CH 3 CH 2 OH CH 3 CH 2 CHO (CH 3 ) 2 CO HOCH 2 CH 2 OH C 2 H 5 OCH 3 (CH 2 OH) 2 CO NH CN N 2 NH 2 HCN HNC N 2 H + NH 3 HCNH + H 2 CN HCCN C 3 N CH 2 CN CH 2 NH HC 2 CN HC 2 NC NH 2 CN C 3 NH CH 3 CN CH 3 NC HC 3 NH + *HC 4 N C 5 N CH 3 NH 2 CH 2 CHCN HC 5 N CH 3 C 3 N CH 3 CH 2 CN HC 7 N CH 3 C 5 N? HC 9 N HC 11 N NO HNO N2O HNCO NH2CHO SH CS SO SO+ NS SiH *SiC SiN SiO SiS HCl *NaCl *AlCl *KCl HF *AlF *CP PN H 2 S C 2 S SO 2 OCS HCS+ c-SiC 2 *SiCN *SiNC *NaCN *MgCN *MgNC *AlNC H 2 CS HNCS C 3 S c-SiC 3 *SiH 4 *SiC 4 CH 3 SH C 5 S FeO DEMIRM

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HI traces diffuse gas; molecular gas traces denser gas & active regions CO on Optical (Wilson & Scoville 1999) CO on HI (Wang, Fall & Whitmore 2001)

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 not Simulation confirms that 3-D geometry is suitable for this interpretation