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 Dark cloud cores 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)

4. t < 10 4 yrs (Shu, Adams & Lizano 1987; Lada 1987) Collapse -- Class 0 SED: T~30 K

5. t ~10 5 yrs (Shu, Adams & Lizano 1987; Lada 1987) Late Collapse -- Class I SED slope,  > 0, for 2 < < 22  m

6. t ~10 6 -10 7 yrs Accretion Disk Stage -- Class II SED slope, 0 >  > -2 2 < < 22  m

7. t > 10 7 yrs Debris or no Disk -- Class III SED slope,  < -2 2 < < 22  m

8. Massive Star Formation -- Competing theories Analogous to low-mass (McKee & Tan 2003) Mergers in dense clusters (Bonnell & Bate 2002) 0.5 pc5 pc Disk formation, collimated outlfows Disk disruption less 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. Spitzer Space Telescope Launched Aug. 2003 Earth trailing orbit 0.85m telescope Instruments include IRAC (3.6,4.5,5.8,8.0  m), MIPS (24, 70,160  m), IRS (5-30  m) ~ 5 year lifetime

11. 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  m new project, MIPSGAL, will get 24, 70, 160 ! (PI: Sean Carey) b=[-1,+1], |l|=10-65 GLIMPSE II: |l|<10 ! Angular resolution <2” PI: Ed Churchwell www.astro.wisc.edu/glimpse

12. 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

13. Example of cluster formation? tens of pc

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

15. 320.23-0.29 Ch 1,2,4 2MASS

17. 332.73-0.61

18. 317.35+0.0 1-2-4 3x2 deg

19. 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)

20. 2-D YSO Model Geometry Rotationally-flattened infalling envelope (Ulrich 1976) Flared disk Partially evacuated outflow cavity

21. A V through Envelope & Disk Edge-onPole-on

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

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

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

25. IRAS 04368+2557 2MASS J-H-KSpitzer IRAC [3.6]-[4.5]-[8.0]

26. Low- mass Analog?

27. Massive protostars

28. L * =40000 T * =4000 M * =17.5 M=10 -4 M d =1 Embedded Massive YSO iAvAv 06 6053 903e4.

29. Embedded Massive YSO - No Cavity iAvAv 045 6068 903e4 L * =40000 T * =4000 M * =17.5 M=10 -4 M d =1.

30. Embedded Low-Mass YSO iAvAv 06 6050 904e6 L * =1.1 T * =4000 M * =1 M=10 -5 M d =0.05.

31. Massive Star+Disk iAvAv 00 600.1 903e3 L * =40000 T * =30000 M * =17.5 M d =0.1

32. Low-Mass Star + Disk iAvAv 00 600.1 903e5 L * =40000 T * =4000 M * =17.5 M d =0.01

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

34. Show luminosity effects Make a plot of integrated flux vs inclination. Don’t have one now, so hide slide.

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

36. 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)

37. 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 r 0 to r -2.5 Smooth-to-clumpy ratio is varied from 3% to 100% (Indebetouw et al. 2005)

38. Temperature and density in an azimuthal slice

39. Indebetouw et al. (2005) IRACMIPS 3-D clumpy models NIR

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

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

42. G5.89 Model parameters T star 41000 K L2.54x10 5 R in 0.0001 pc R out 2.5 pc M env 50000 A v_ave 131 Smooth/Clumpy10% Radial density  ave ~r 0 Fractal dimension2.6

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

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

45. 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)

46. 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 A V –Can process the GLIMPSE survey in about a week (Robitaille et al. 2005)

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

48. Grid Parameters

49. 198,680 SEDs

50. Color-color plots--Envelope infall rate No envelope (disk) <10 -7 M sun /yr 10 -7 -10 -4 M sun /yr >10 -4 M sun /yr

51. Synthetic cluster Color-color plots -- 2MASS D=4 kpc (RCW 49) GLIMPSE low/high sensitivity limits Class I Class II Class III all

52. Color-color plots--Envelope infall rate No envelope (disk) <10 -7 M sun /yr 10 -7 -10 -4 M sun /yr >10 -4 M sun /yr

53. Color-color plots--Disk only M disk < 10 -7 M* 10 -7 M * < M disk < 10 -4 M * 10 -4 M * < M disk < 10 -2 M * M disk >10 -2 M *

54. Color-color plots--Disk only M disk < 10 -7 M* 10 -7 M * < M disk < 10 -4 M * 10 -4 M * < M disk < 10 -2 M * M disk >10 -2 M *

55. Relating Observed Class to Model “Stage” ClassSpectral Index (2- 20  m) I>0 II-2 - 0 III<-2 StageEnvelope Infall rate (M sun /yr/ M * 1/2 ) Disk mass (M * ) I>2x10 -6 II<2x10 -6 >1x10 -7 III0<1x10 -7

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

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

58. 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 A V (Robitaille et al. 2005)

59. Fitter results on a single source

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

61. RCW 79

62. 98.5% of sources fit with stellar atmospheres 0.4% well-fit with YSO models Class I source

63. RCW 49

64. 96.6% of sources fit with stellar atmospheres 3% well-fit with YSO models Class I source

65. IC348 Mass histogram “Known” IMF (using prior information on stellar parameters) Data from Lada et al. (2005)

66. IC348 Mass histogram Based on Model Fitter Only

67. RCW 49 Synthetic Mass histogram Sampled masses from grid using Salpeter IMF (flatter slope below 0.5 M sun ) Sampled ages using Taurus ratios (Kenyon & Hartmann 1995) Apply GLIMPSE sensitivy limits

68. RCW 49 Fitted Mass histogram Use model fitter to determine masses

69. RCW 49 Histograms

70. Galactic Extinction map

71. RCW 49 - Giant H II Region (GLIMPSE)

72. Applications of Grid & Fitter Study Global properties of star formation in Galaxy –Star formation rate, lifetimes of evolutionary states, IMF –A high star formation efficiency argues for turbulent cloud support (vs. magnetic) Search for disks around massive stars –Adds further credence to accretion model for high-mass star formation –Disks form planets

73. …applications Study low-mass star formation in vicinity of high-mass –May be more common mode of star formation (Hester & Desch 2004) –Disk lifetimes, sizes 3-D extinction map Galactic structure –80% of stars are K giants –Fitter can distinguish gravity (I.e., giants/MS)

74. Future Work Radiative Transfer –Add PAHs, stochastic heating of small grains Grid and fitter will be publicly available in 2006 RT codes available at http://gemelli.spacescience.org/~bwhitney/codes