The Velocity Structure of the Interstellar Medium: Ongoing Projects

Velocity Coherence

Two papers were submitted to the Ap.J. on "Velocity Coherence in Dense Cores" in early 1997, and have their own web site. The results of the project suggest that star-forming low-mass dense cores are akin to "islands of calm in a turbulent sea," in that the medium around cores is far more turbulent and structured than the cores themselves. The model predicts that the internal structure of these cores should not be self-similar to the same extent as is the larger-scale ISM. Several new observing projects are in progress to test these ideas.


The Spectral Correlation Function (SCF) Method

This method provides a new diagnostic of spectral-line maps, aimed at identifying physically meaningful velocity structure in the ISM. A large grid of spectra (a.k.a. a data cube) is analyzed in order to determine how similar neighboring spectra are to one another. The method differs from more traditional Autocovariance Function (ACF) or Structure Function (SF) analyses (see Scalo 1984), in that it preserves spatial information. Both the ACF and SF methods can produce information on how spectral properties vary with the separation of spectra in a map. The new "SCF" method gives this information, along with information on where in a map spectral changes occur. These changes can be quantitatively correlated with changes in other mapped parameters, such as mean velocity, line width, integrated intensity, antenna temperature, etc.

The original formalism for this method is described in a short PDF file, which the interested reader is urged to consult. The current form of the SCF method is described in Roslowsky (1998) which is available here in PDF form.

Gradient Filtering

Building on the velocity gradient-fitting methods developed in Goodman et al. (1993), we are working on a new technique for measuring the variations in velocity gradient within a large region of the ISM. The program under development goes through a large data cube and fits a gradient to a localized region around each observed position. (Parts of the algorithm are similar to the SCF method above.) The resolution of the gradient filter can be adjusted so that maps of the "gradient field" (which should relate to the vorticity field) relevant to a range of spectral scales can be produced.


The Goal: Using SCF, Gradient Filtering, on Data and Simulations

The SCF and Gradient Filtering techniques are intended to be used both on "real" and "simulated" data. The advantage of using the techniques on simulated data is that the physical inputs to the simulations can be adjusted and understood. Presumably, if the SCF and Gradient Filters are good discriminants, then only a small subset of the simulations will match the data! It is those simulations which we expect to best represent the true ISM.

The simulations most relevant to the dense regions (>100 ptcl/cc) of the ISM to which our studies pertain are being carried out by Charles Gammie, Eve Ostriker, and Jim Stone. Their simulations can give three-dimensional views of the velocity, density, and magnetic field structure in magnetized, self-gravitating, dense interstellar gas. An earlier work, by Gammie & Ostriker, showed that 1+2/3D simulations provide evidence that nonlinear hydromagnetic waves can support self-gravitating molecular clouds.

If you're very interested in our plans in this area, you might want to check out a recent proposal submitted to the NSF and NASA to extend this work (original NASA version available here, but NSF version is recommended.)

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