Star Formation Taste Tests. Alyssa A. Goodman Harvard-Smithsonian Center for Astrophysics & Initiative for Innovative Computing at Harvard
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1 Star Formation Taste Tests Alyssa A. Goodman Harvard-Smithsonian Center for Astrophysics & Initiative for Innovative Computing at Harvard
2 Taste Tests? Taste Tests? We frame this project by analogy. How does a great chef, making a complicated dish, know if she has created what she originally intended when she is done cooking? She tastes. She informs her cooking with her extensive knowledge of food chemistry (analytic theory), uses all the cooking equipment (simulations) she has in the kitchen to try to make something edible and tasty (starforming, and realistic), and then she uses her senses (observations) to see if what she made tastes as intended. Tasting in cooking actually encompasses the joint action of many senses: we propose here a combination of statistical techniques that we call taste tests. The tests will allow us to discerningly decide if what we sense (observe) and what we can cook (simulate) might actually be tasty (form stars), and how (analytic theory) that happens. from: Goodman & Rosolowsky, NSF Proposal Fall 2006
3 Getting to Observational Space Includes Radiative Transfer Projection to 2D sky plane, or 3D of spectral-line data cubes Adding appropriate noise Imposing observing characteristics of a telesope Example: The Spectral Correlation Function (Padoan, Goodman & Juvela 2003)
4 = COordinated Molecular Probe Line Extinction Thermal Emission Survey of Star-Forming Regions + Taste Tests COMPLETE Collaborators, Spring 2007: Alyssa A. Goodman (CfA/IIC) João Alves (Calar Alto, Spain) Héctor Arce (AMNH) Michelle Borkin (CfA/IIC) Mark Heyer (UMASS/FCRAO) Jason Li (Harvard College) Paola Caselli (Arcetri, Italy/CfA) Doug Johnstone (HIA, Canada) Jaime Pineda (CfA, PhD Student) James DiFrancesco (HIA, Canada) Jens Kauffmann (CfA/IIC) Erik Rosolowsky (CfA) Jonathan Foster (CfA, PhD Student) Helen Kirk (HIA, Canada) Scott Schnee (Caltech) Sebastian Guillot (U. Victoria, Canada) Di Li (JPL) Mario Tafalla (OAN, Spain)
5 = COordinated Molecular Probe Line Extinction Thermal Emission Survey of Star-Forming Regions Is the gas density distribution lognormal or not? Meaningful structure in position-position-velocity space And scattering! (Cloudshine models) Modeling l.o.s. temperature fluctuations What stars form from what gas, when? COMPLETE Collaborators, Spring 2007: Alyssa A. Goodman (CfA/IIC) João Alves (Calar Alto, Spain) Héctor Arce (AMNH) Michelle Borkin (CfA/IIC) Mark Heyer (UMASS/FCRAO) Jason Li (Harvard College) Paola Caselli (Arcetri, Italy/CfA) Doug Johnstone (HIA, Canada) Jaime Pineda (CfA, PhD Student) James DiFrancesco (HIA, Canada) Jens Kauffmann (CfA/IIC) Erik Rosolowsky (CfA) Jonathan Foster (CfA, PhD Student) Helen Kirk (HIA, Canada) Scott Schnee (Caltech) Sebastian Guillot (U. Victoria, Canada) Di Li (JPL) Mario Tafalla (OAN, Spain)
6 Modeling line-of-sight temperature fluctuations Errors introduced by the assumption of isothermal dust along each line of sight Variable fraction of emission from transiently heated very small dust grains log (N IRAS [mag A V ]) IRIS greyscale shows log (N 13CO [mag A V ]) Variable dust properties (e.g. emissivity or emissivity spectral index) MASS/NICER log (N 2MASS [mag A V ]) Schnee, Bethell & Goodman 2006
7 Modeling line-of-sight temperature fluctuations MHD Simulation+Radiative Xfer Code (No NOISE) A V From Emission /100 micron A V From Emission /240 micron and the correlation gets tighter still at longer λ s True A V True A V Schnee, Bethell & Goodman 2006
8 Modeling line-of-sight temperature fluctuations Tasting the Simulations A V From Emission 60/100 True A V A V from NIR A V from NIR I ll show you later why that s True Av Schnee, Bethell & Goodman 2006
9 Extinction and scattering!ambient (Cloudshine) Cloudshine =Scattered Starlight 2006
10 Almost Extinction Tasting and scattering! a Very Simple (Cloudshine) Recipe Foster & Goodman 2006
11 Extinction and scattering! Theorists doing the (Cloudshine) Tasting! Simulation H-band flux only Cloudshine Scattering Model Recovered map Tastes right, with 20% scatter, at 1<AV<10, for NIR. Padoan et al. 2006
12 Where am I? Modeling l.o.s. temperature fluctuations And scattering! (Cloudshine models) Spectral-Line Data: The 2D (bland)& the 3D (Spicy) Views Is the gas density distribution lognormal or not? (2D) Meaningful structure in position-position-velocity space (3D) What stars form from what gas, when?
13 position-postion-velocity is NOT the same as position-position-position-velocity-velocity-velocity mm peak (Enoch et al. 2006) cf. Ostriker, Stone & Gammie 2001 et al. 2005, Kirk et al. 2006) sub-mm peak (Hatchell 13CO (Ridge et al. 2006) mid-ir IRAC composite from c2d data (Foster, Laakso, Ridge, et al. in prep.) Optical image (Barnard 1927) 3D rendering courtesy AstroMed team at IIC (Borkin, Halle, Kauffmann, Alan, Goodman)
14 The (secret) uncertainties inherent in column density mapping. Goodman et al Is the gas density distribution lognormal or not? ~5 (2D) pc 33 Extinction Dust Emission 13 CO Emission Declination [J2000, Degrees] Declination [J2000, Degrees] Declination [J2000, Degrees] Extinction 1-σ uncertainty (NICER/2MASS) (NICER) Right Ascension [J2000, Degrees] shell Dust Dust Color Emission Temperature (IRAS) (IRAS) HD Line Opacity Emission ( 13 CO) ( 13 CO) Right Ascension [J2000, Degrees] Right Ascension [J2000, Degrees] Equivalent Equivalent A A V [mag] V [mag] 60 to Equivalent 100µ Color A Temperature V [mag] [K] Equivalent τ ( 13 CO) A V [mag]
15 Implied Column Density Distributions and lognormal Fits (Perseus COMPLETE data) Is the gas density distribution lognormal or not? (2D) 400 Extinction & thermal emission are log-normal-ish & more so when non- 13 CO detected points are included. Number Number lognormal fit to 2MASS column density (all panels) IRAS 2MASS/NICER lognormal fit to IRAS column density CO is not a very faithful tracer of column density. Number lognormal fit to 13 CO CO 0.5 W( 13 CO ) log (Equivalent A V [mag])
16 Dendrograms Meaningful (Hierarchical) structure in vs. position-position-velocity CLUMPFIND (Non-hierarchical) space (3D) Dendrogram (Rosolowsky et al. 2007; cf. Houlahan & Scalo 1992) CLUMPFIND (Williams et al. 1994)
17 Meaningful structure in position-position-velocity space (3D) (Dendro)Surfaces CLUMPFIND Observed Reality Sky y (Declination) Velocity Sky x (Right Ascension) Observed Simulations work of Rosolowsky, Pineda, Kauffmann, Borkin,Padoan, Halle & Goodman; figure from Goodman & Rosolowsky NSF Star Formation Taste Tests Proposal, Fall 2006
18 Is CLUMPFIND OK as a Statistic? (Like Cayenne Pepper?) step 0.3 step 0.5 threshold 0.3 threshold 0.5 CLUMPFIND output for L1448 with 1.2K step & threshold! (Lower values give too many clumps to show!!) threshold 0.7
19 Dendrograms: A Physical Hierarchy? The selfgravitating parts of L1448: Rosolowsky, Borkin, Goodman, Kauffmann & Pineda 2007, in prep.
20 And, coming soon, to a cookbook (Journal) near you... (3D PDF)
21 Figure Credit: Jonathan Foster Which stars form from what gas, when? L1448
22 What stars form from what gas, when? Theorists using Observers Ingredients e.g. Schmeja & Klessen 2006
23 Are you hungry yet?
24 Who can make this?
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