Observational Magnetohydrodynamics of the Interstellar Medium Alyssa A. Goodman Harvard-Smithsonian Center for Astrophysics Principal Collaborators Héctor Arce, CfA Javier Ballesteros-Paredes, AMNH Sungeun Kim, CfA Paolo Padoan, CfA Erik Rosolowsky, UC Berkeley Enrique Vazquez-Semadeni, UNAM Jonathan Williams, U. Florida David Wilner, CfA

Observational MHD of the ISM The Good Old Days Low Resolution, Observationally & Computationally Spherical, Smooth, Long-lasting “Cloud” Structures And “structure” came from fragmentation

Observational MHD of the ISM The “New Age” High(er) Resolution, Observationally & Computationally Highly irregular structures, many of which are “transient” on long time scales

“Observational” MHD of the ISM The “New Age” High(er) Resolution, Observationally & Computationally Highly irregular structures, many of which are “transient” on long time scales Stone, Gammie & Ostriker 1999 Stone, Gammie & Ostriker 1999 And “structure” comes from turbulence on all but smallest scales

What can we actually observe? Intensity(position, position,velocity) Falgarone et al. 1994

Velocity is the Observer’s "Fourth" Dimension Loss of 1 dimension No loss of information

How Many Bits? Product N *N N S/N in 1 hour, S/N (S/N)*N N Year 2000 1990 1980 1970 1960 1950 Year 10 2 3 4 5 6 7 8 (S/N)*N pixels *N channels 10 1 2 3 4 N channels, S/N in 1 hour, pixels Product N channels S/N N pixels

Statistical Tools Can no longer examine “large” spectral-line maps or simulations “by-eye” Need powerful, discriminatory tools to quantify and intercompare data sets Previous attempts are numerous: ACF, Structure Functions, Structure Trees, Clumpfinding, Wavelets, PCA, D-variance, Line parameter histograms Most previous attempts discard or compress either position or velocity information

1997 Goals of the “Spectral Correlation Function” Project Develop “sharp tool” for statistical analysis of ISM, using as much data of a data cube as possible Compare information from this tool with other statistical tools applied to same cubes Incorporate continuum information Use best suite of tools to compare “real” & “simulated” ISM Adjust simulations to match, understanding physical inputs Develop a (better) prescription for finding star-forming gas

Strong vs. Weak B-Field Stone, Gammie & Ostriker 1999 Driven Turbulence; M K; no gravity Colors: log density Computational volume: 2563 Dark blue lines: B-field Red : isosurface of passive contaminant after saturation b = T / 10 K [ ] n H 2 100 cm -3 B 1 . 4 m G

The Spectral Correlation Function v.1.0 Simply measures similarity of neighboring spectra (Rosolowsky, Goodman, Wilner & Williams 1999) S/N equalized, observational/theoretical comparisons show discriminatory power v.2.0 Measures spectral similarity as a function of spatial scale (Padoan, Rosolowsky & Goodman 2001) Noise normalization technique found SCF(lag) even more powerful discriminant Applications Finding the scale-height of face-on galaxies! (Padoan, Kim & Goodman 2001) Understanding behavior of atomic ISM (e.g. Ballesteros-Paredes, Vazquez-Semadeni & Goodman 2001)

How SCF v.1.0 Works Measures similarity of neighboring spectra within a specified “beam” size lag & scaling adjustable signal-to-noise accounted for See: Rosolowsky, Goodman, Wilner & Williams 1999; Ballesteros-Paredes, Vazquez-Semadeni & Goodman 2001

Application of the “Raw” SCF Antenna Temperature Map Application of the “Raw” SCF greyscale: TA=0.04 to 0. 3 K “Raw” SCF Map Data shown: C18O map of Rosette, courtesy M. Heyer et al. Results: Padoan, Rosolowsky & Goodman 2001 greyscale: while=low correlation; black=high

Application of the SCF Antenna Temperature Map “Normalized” SCF Map greyscale: TA=0.04 to 0. 3 K Antenna Temperature Map Application of the SCF “Normalized” SCF Map Data shown: C18O map of Rosette, courtesy M. Heyer et al. Results: Padoan, Rosolowsky & Goodman 2001. greyscale: while=low correlation; black=high

SCF Distributions Randomized Positions Original Data Normalized C18O Data for Rosette Molecular Cloud Original Data Randomized Positions

Insights from SCF v.1.0 Rosolowsky, Goodman, Williams & Wilner 1999 Unbound High-Latitude Cloud Observations Self-Gravitating, Star-Forming Region Insights from SCF v.1.0 Rosolowsky, Goodman, Williams & Wilner 1999 No gravity, No B field No gravity, Yes B field Yes gravity, Yes B field Simulations

Which of these is not like the others? Increasing Similarity of Spectra to Neighbors 1.0 0.8 0.6 0.4 0.2 0.0 1.2 1.0 0.8 0.6 0.4 0.2 0.0 Increasing Similarity of ALL Spectra in Map L134A 12CO(2-1). L1512 12CO(2-1) Pol. 13CO(1-0) L134A 13CO(1-0) HCl2 C 18O Peaks HCl2 C 18O Rosette C 18O Rosette C 18O Peaks SNR H I Survey Rosette 13CO Rosette 13CO Peaks HLC Change in Mean SCF with Randomization G,O,S MacLow et al. Falgarone et al. Mean SCF Value

v.2.0: Scale-Dependence of the SCF Example for “Simulated Data” Padoan, Rosolowsky & Goodman 2001

“A Robust Statistic” Padoan, Rosolowsky & Goodman 2001

Galactic Scale Heights from the SCF (v.2.0) HI map of the LMC from ATCA & Parkes Multi-Beam, courtesy Stavely-Smith, Kim, et al. Padoan, Kim & Goodman 2001

Insights into Atomic ISM from SCF (v.1.0) Comparison with simulations of Vazquez-Semadeni & collaborators shows: “Thermal Broadening” of H I Line Profiles can hide much of the true velocity structure SCF v.1.0 good at picking out shock-like structure in H I maps (also gives low correlation tail) See Ballesteros-Paredes, Vazquez-Semadeni & Goodman 2001.

How is the MHD turbulence driven in the dense ISM? pc-scale outflows?

“Giant” Herbig-Haro Flows: PV Ceph 1 pc Reipurth, Bally & Devine 1997

Giant HH Flow in PV Ceph 12CO (2-1) OTF Map from NRAO 12-m 67:40 67:45 67:50 67:55 20:46 20:45 PV Cephei H 3 Giant HH Flow in PV Ceph 1 5 B H 3 C HH 315D a ( 9 ) d HH 215P1 215P2 4 E F A 12CO (2-1) OTF Map from NRAO 12-m Red: 3.0 to 6.9 km s-1 Blue: -3.5 to 0.4 km s-1 Arce & Goodman 2001

Driving Turbulence with Outflows Studies in Héctor Arce’s Ph.D. Thesis (Harvard, 2001; see Arce & Goodman 2001 a,b,c,d) show: HH 300 outflow has ~enough power (~0.5 Lsun at a 1-pc scale) to drive turbulence in its region of Taurus (using estimates based on Gammie & Ostriker 1996) Many outflows show clear evidence for “episodicity” and this may effect coupling of outflow energy to cloud Episodicity may also explain steep mass-velocity relations, and odd-looking p-v diagrams Outflow sources move through the ISM (e.g. PV-Ceph)

NGC 2264G g~1.8 g~3.5 Lada, C.J. & Fich, M. 1996, ApJ,.459, 638

L1551

L1448

FIG. 10. Northeast lobe of the IRS 1 flow FIG. 10.Northeast lobe of the IRS 1 flow. (a) Area-integrated spectrum of region R2 in 12CO J = 2 1 (solid line), 12CO J = 10 (dotted line), and 13CO J = 10 (dashed line). (b) Fit to the ratio of optical depths R12/13 = (12CO J = 10)/(13CO J = 10), integrated over the region. Valid points kept for the fit are those with ratios with both intensities above twice the rms noise and for velocities outside the turbulent line core (diamonds). Invalid points not meeting those criteria are indicated by a cross. The second-order polynomial fit to the valid points is shown as a solid curve. The dashed line is the result of fitting a parabola to the entire cloud in region R1. Vertical dashed lines outline the turbulent line core (cloud vLSR ± 0.75 km s-1). (c) Luminosity mass vs. inclination-corrected velocity from center of the flow. Lines show fits to the power law ML v-. Points for the blueshifted lobe are indicated by diamonds; the redshifted lobe points by triangles. Filled symbols denote masses calculated directly from 13CO J = 10; open symbols denote masses calculated from the fit of the optical depth ratio. (d, e, and f) Same as (a), (b), and (c) except that they are for the southwest lobe of the IRS 1 flow (region R3). FIG. 11.Northeast lobe of the IRS 1 flow with the ambient cloud subtracted out. (a) Area-integrated 12CO J = 21 emission in region R2 (thin line); same emission with the ambient cloud (defined by region R4) subtracted out (thick line). (b and c) Same as for Fig. 10. (d, e, and f) Same as (a), (b), and (c) except that they are for the southwest lobe of the IRS 1 flow (region R3 in Fig. 9) with the ambient cloud (region R5 in Fig. 9) subtracted out. B5 Yu, Billawala, Bally, 1999

Sample outflow position-velocity diagrams Lada & Fich 1996 Sample outflow position-velocity diagrams Bachiller, Tafalla & Cernicharo 1994 B5 Yu Billawala & Bally 1999 L1448 Bachiller et al. 1990

Outflow position-velocity diagrams Arce & Goodman 2001

Variations in Burst History… e.g. NGC2264 Single or Dominant “Hubble” Flow e.g. L1448 Sorted “Hubble” Wedges Velocity e.g. B5, HH300 Random “Hubble” Wedges Position

Mass-Velocity & Position-Velocity Relations in Episodic Outflows 10 10 Power-law Slope of Sum = -2.7 A,B,C... for constant vmax a,b,c... for varying vmax (alphabetical=chronological) Slope of Each Outburst = -2 10 -1 8 a 1 10 -2 Mass [Msun] Maximum Velocity, vmax 6 10 -3 2 4 10 -4 A d B C 3 10 -5 D 4 E 2 c 5 6 7 e b 8 2 3 4 5 6 7 8 2 3 4 5 6 7 8 2 0.1 1 10 2 4 6 8 10 Velocity [km s-1] Maximum Offset from Source, dmax Arce & Goodman 2001

Time-Ordering of p-v Diagrams? c b a d e c b a d e c b

Episodic ejections from precessing or wobbling moving source PV Ceph Episodic ejections from precessing or wobbling moving source Required motion of 0.25 pc (e.g. 2 km s-1 for 125,000 yr or 10 km s-1 for 25,000 yr) Arce & Goodman 2001

Even leaves a trail? Arce & Goodman 2001

Outflows Driving Turbulence What you see (now) is not the whole story. Outflows seem to have a complex time-history. Sources may travel. Questions Raised: Is true net momentum/energy input is still measurable from observations? Do simulations need to include time history, or is “net” enough? How do we find all the flows?