1. 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

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

3. 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

4. “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

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

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

7. 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

8. 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

9. 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

10. 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

11. 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)

12. 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

13. 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

14. 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

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

16. 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

17. 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).
L CO(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

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

19. “A Robust Statistic”
Padoan, Rosolowsky & Goodman 2001

20. 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

21. 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.

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

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

25. 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

26. 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)

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

28. L1551

29. L1448

30. 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

31. 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

32. Outflow position-velocity diagrams
Arce & Goodman 2001

33. 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

34. Mass-Velocity & Position-Velocity Relations in Episodic Outflows10 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

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

36. 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

37. Even leaves a trail?
Arce & Goodman 2001

38. 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?