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Small Ionized and Neutral Structures in the Diffuse ISM Socorro NM May 22, 2006

Structure of the Local ISM Jeffrey Linsky (University of Colorado) Seth Redfield (University of Texas). Small Ionized and Neutral Structures in the Diffuse ISM Socorro NM May 22, 2006. Objectives of the Program.

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Small Ionized and Neutral Structures in the Diffuse ISM Socorro NM May 22, 2006

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  1. Structure of the Local ISMJeffrey Linsky (University of Colorado)Seth Redfield (University of Texas) Small Ionized and Neutral Structures in the Diffuse ISM Socorro NM May 22, 2006

  2. Objectives of the Program • Develop a three-dimensional model for the warm gas clouds inside the Local Bubble (within log NHI = 19.2, about 100 pc). • Based on kinematics (common velocity vectors) and common physical properties (T, ξ, D(Fe), and D(Mg)). • Using high-resolution UV spectra from HST/GHRS, HST/STIS, ground-based Ca II, and future HST/COS. • As a first approximation, assume that “clouds” are rigid (ΔV<1.0 km/s) and homogeneous structures. • Will compare with ionization models (e.g., Slavin & Frisch 2002) and models of transition layer heating by the hot gas in the Local Bubble.

  3. Previous Studies of the Structure of the Local Interstellar Medium • Several previous studies based only on kinematic information. • Lallement & Bertin (1992) identified the AG (now called LIC) and the G clouds. • Frisch, Grodnicki, & Welty (2002) identified 7 clouds (LIC, Blue, Aql/Oph (piece of G), Peg/Aqr (piece of Eri), North Pole (piece of NGP), South Pole (?), Filament (?).

  4. Technique for Identifying Individual Clouds Inside the Local Bubble: I • The LIC velocity vector jumps out of the data. • Compute ΔV from this vector for all (146 for UV data and another 86 Ca II) velocity components. • Delete coarse ΔV outliers and compute a new velocity vector. • Iterate (decrease ΔV criterion to <1.0 km/s) until have a velocity vector with a very low Χ². • Include in LIC only lines of sight that form a continuous structure (blue dots). Green dots meet the kinematics requirement by are excluded for other reasons.

  5. Technique for Identifying Individual Clouds Inside the Local Bubble: II • Compute ΔV from the LIC vector for all data. • Look for groupings of adjacent lines of sight with similar ΔV from LIC and compute a velocity vector. • Sequentially remove velocity outliers from the remaining data set until compute a velocity vector with ΔV<1.0 km/s criterion. • Include in cloud only contiguous data points. • Look for other groupings of lines of sight with close values of ΔV from the LIC vector and continue. • Use physical parameters when available to purge inconsistent lines of sight from the clouds

  6. Measuring Cloud Temperature and Nonthermal Velocity from STIS Data

  7. Correspondence of Location of Scattering Screens with Cloud Edges

  8. For the Future • Add new lines of sight from STIS, GHRS, Ca II high-resolution spectra. • Add new physical property data from STIS. • Attempt to determine three-dimensionality from new spectra (COS, refurbished STIS ?). • Search for departures from rigid kinematics (rotation, shears, etc.). • Compare with ionization models. • Compare with C IV, N V, and O VI (FUSE) lines for evidence of transition layers and heating.

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