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Radiative Transfer Models of Dusty YSOs

Radiative Transfer Models of Dusty YSOs. Barbara Whitney (Space Science Institute), Tom Robitaille & Kenny Wood (St. Andrews University), Jon Bjorkman (U. Toledo), Remy Indebetouw (U Va), Ed Churchwell (UW). Outline. Background and Motivation

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Radiative Transfer Models of Dusty YSOs

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  1. Radiative Transfer Models of Dusty YSOs Barbara Whitney (Space Science Institute), Tom Robitaille & Kenny Wood (St. Andrews University), Jon Bjorkman (U. Toledo), Remy Indebetouw (U Va), Ed Churchwell (UW)

  2. Outline • Background and Motivation • Large Volumes of mid-IR data now available from Spitzer Space Telescope, ground-based observatories and future space-based • e.g., the GLIMPSE survey of the inner Galactic Plane • Unanswered questions • 2-D Models • 3-D Models (high mass) • Model Grid & Fitter • Answers to questions? A few, maybe

  3. Canonical View of Low-Mass Star Formation • Free-fall times short, yet star formation efficiency low (Zuckerman & Evans 1974) • Conditions for support/collapse • Magnetic fields/Ambipolar diffusion (Shu 1977; Mouschovias 1976; Nakano 1976) • Supersonic turbulence/local collapse (Mac Low & Klessen 2004) Dark cloud cores

  4. Collapse -- Class 0 t < 104 yrs SED: T~30 K (Shu, Adams & Lizano 1987; Lada 1987)

  5. Late Collapse -- Class I SED slope, > 0, for 2 <  < 22 m t ~105 yrs (Shu, Adams & Lizano 1987; Lada 1987)

  6. Accretion Disk Stage -- Class II SED slope, 0 > > -2 2 <  < 22 m t ~106-107 yrs

  7. Debris or no Disk -- Class III SED slope, < -2 2 <  < 22 m t > 107 yrs

  8. Massive Star Formation -- Competing theories 0.5 pc 5 pc Analogous to low-mass (McKee & Tan 2003) Mergers in dense clusters (Bonnell & Bate 2002) Disk formation, collimated outlfows Disk disruptionless collimated flows

  9. Questions • What are the global properties of star formation in the Galaxy? (GLIMPSE) • Star formation rate and efficiency • Timescales for evolution • How do massive stars form? • Do they form planets? • Do low-mass stars in the vicinity of massive stars form planets? • What supports clouds against collapse?

  10. Galactic Legacy Infrared Mid-Plane Survey Extraordinaire • One of five Spitzer Legacy programs • No proprietary period + enhanced data products • 4 wavelength bands: 3.6, 4.5, 5.8, 8 mnew project, MIPSGAL, will get 24, 70, 160 !(PI: Sean Carey) • b=[-1,+1], |l|=10-65GLIMPSE II: |l|<10 ! • Angular resolution <2” PI: Ed Churchwell www.astro.wisc.edu/glimpse

  11. GLIMPSE Data Products* • GLIMPSE Point Source Catalog • Highly reliable (>99.5%) -- 31 million sources • Magnitude limits in 4 bands: 14.2, 14.1, 11.9, 9.5 • GLIMPSE Point Source Archive • Less reliable but more complete -- 48 million sources • Magnitude limits: 14.5, 14.0, 13.0, 11.5 • Cleaned mosaic images • 1.1x0.8 degrees (0.6” pixels) • 3x2 degrees (1.2” pixels) • Southern hemisphere available in Dec. (all Spitzer “BCD” images and mosaiced AORs are available) *Available at http://www.astro.wisc.edu/glimpse/glimpsedata.html

  12. Example of cluster formation? tens of pc

  13. Class 0 Source? 324.72+0.34 1-2-4 J-H-K

  14. 320.23-0.29 Ch 1,2,4 2MASS

  15. 332.73-0.61

  16. 317.35+0.0 1-2-4 3x2 deg

  17. Radiative Transfer Models • Monte Carlo method • 3-D spherical polar grid • Calculates radiative equilibrium of dust (Bjorkman & Wood 2001) • Non-isotropic scattering + polarization • Output: images + SEDs (+ polarization) • Not included: PAHs, stochastic heating of small grains, optically thick gas emission (Whitney et al. 2003a,b, 2004)

  18. 2-D YSO Model Geometry • Rotationally-flattened infalling envelope (Ulrich 1976) • Flared disk • Partially evacuated outflow cavity

  19. AV through Envelope & Disk Edge-on Pole-on

  20. Low-Mass Protostar:IRAS 04302+2247 L=0.5 Lsun 2-D RT models NIR 3-color (Padgett et al. 1999)

  21. Spitzer IRAC predictions J-H-K [3.6]-]-[4.5]-[8.0] [24]-[70]-[160] Late Class 0 Class I (Whitney et al. 2003b)

  22. IRAS 04368+2557 2MASS J-H-K Spitzer IRAC [3.6]-[4.5]-[8.0]

  23. Low-mass Analog?

  24. Massive protostars

  25. Embedded Massive YSO L*=40000 T*=4000 M*=17.5 M=10-4 Md=1 .

  26. Embedded Low-Mass YSO L*=1.1 T*=4000 M*=1 M=10-5 Md=0.05 .

  27. Massive Star+Disk L*=40000 T*=30000 M*=17.5 Md=0.1

  28. Low-Mass Star + Disk L*=40000 T*=4000 M*=17.5 Md=0.01

  29. Effect of Bipolar Cavity on Colors IRAC Near-IR • Models without cavities (e.g., 1-D) will underestimate evolutionary stage! No cavity cavity

  30. Massive Stars: The need for 2-D, 3-D models (van der Tak et al. 2000) >100 m: no <100 m: yes

  31. 3-D models • Motivation • UCHII regions: Previous 1-D models of mid-IR spectra can’t fit full SED: give too deep 10 m absorption for a given FIR flux, and too steeply rising SED in NIR/MIR (Faison et al. 1998, van der Tak et al. 2000)

  32. Model Ingredients • O star in a molecular cloud (massive stars heat up large volumes) • Use fractal ISM structure, D=2.6 (Elmegreen 1997) • Average radial density profile is varied from r0 to r-2.5 • Smooth-to-clumpy ratio is varied from 3% to 100% (Indebetouw et al. 2005)

  33. 3-D clumpy models NIR IRAC MIPS Indebetouw et al. (2005)

  34. Clumpy model SEDs Average Smooth (1-D) model 200 sightlines from 1 source (grey lines)

  35. Fits to Data: G5.89-0.39 Mid-IR data: Faison et al. (1998) Best clumpy model Grey lines show other sight lines Best smooth model

  36. G5.89 Model parameters

  37. Color-color plots 200 sightlines from 1 clumpy model Smooth model

  38. All the UCHII Observations Mid-IR data: Faison et al. (1998) Grey lines: G5.89 best model

  39. 3-D Model summary • UCHII regions may be O-B stars still embedded in their natal molecular clouds but not surrounded by infalling envelopes. • Bolometric flux of clumpy models varies by a factor of 2 lower and higher than the true luminosity depending of viewing angle (Indebetouw et al. 2005)

  40. 2-D/3-D Model grid + Data fitter • Large Grid of YSO Models (20,000) x 10 inclinations = 200,000 SEDs! 6 weeks of cpu time on about 50 processors • Linear Regression Fitter to find best model to fit an observed SED • Models are convolved with any broadband filter of interest • First tries to find good fit from a grid of stellar atmosphere files • Simultaneously fits foreground AV • Can process the GLIMPSE survey in about a week (Robitaille et al. 2005)

  41. Grid Creation • Sample stellar mass and age (logarithmically) • calculate T* and R* from evolutionary tracks (Bernasconi & Maeder 1996; Siess et al. 2000)

  42. Grid Parameters

  43. 198,680 SEDs

  44. Relating Observed Class to Model “Stage”

  45. Synthetic cluster Color-color plots -- IRAC • D=4 kpc (RCW 49) • GLIMPSE low/high sensitivity limits • “Stage I” • Stage II • Stage III • all Reddening line stars

  46. Class vs Stage • Classification spectral index was defined over wavelength range of 2-22 m (Lada 1987). • What happens for 2-I?

  47. Motivation for Fitter • Fit as many datapoints as available simultaneously • Unbiased (except for grid choices) -- shows all fits to a given dataset • Estimates uncertainties • Estimates foreground AV (Robitaille et al. 2005)

  48. Fitter results on a single source

  49. GLIMPSE Empty Field • 99.6% of sources fit with stellar atmospheres • 0.4% evolved stars, bad data or YSOs?

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