1. Automated Fitting of High-Resolution Spectra of HAeBe stars Improving fundamental parameters Jason Grunhut Queen’s University/RMC

2. Motivation Common ways to determine temperature Common ways to determine temperature Photometry Photometry SED SED Problems Problems Extinction/emission and calibrations Extinction/emission and calibrations Many corrections necessary Many corrections necessary Take advantage of high-res ESPaDOnS spectra Take advantage of high-res ESPaDOnS spectra Minimal corrections required Minimal corrections required

3. Full ESPaDOnS Spectral Range 11000 K synthetic model

5. Spectrum variation with temperature from nearest Kurucz models (±500 K)

7. Automated Fitting of Spectra Search through a pre-defined grid of synthetic spectra. Search through a pre-defined grid of synthetic spectra. 4200-5200 Angstroms 4200-5200 Angstroms Solar abundances. Solar abundances. Most current VALD line list. Most current VALD line list. Micro-turbulent velocity of 2 km/s. Micro-turbulent velocity of 2 km/s. No macro-turbulence. No macro-turbulence. Models computed using synth3 Models computed using synth3 Grid from 6500-35000 K, log(g) from 3.0-5.0 Grid from 6500-35000 K, log(g) from 3.0-5.0 100 K resolution up to 20000 K, 200 K resolution from then up. 100 K resolution up to 20000 K, 200 K resolution from then up.

8. How Program Works Radial velocity is first determined based on suggested model. Radial velocity is first determined based on suggested model. Projected rotational velocity is fit for each model in the specified range (computed using slightly modified s3dIV code). Projected rotational velocity is fit for each model in the specified range (computed using slightly modified s3dIV code). Model with minimum chi-square represents best fit. Model with minimum chi-square represents best fit. Radial velocity is fit for a final time for best model. Radial velocity is fit for a final time for best model.

9. Theoretical Results for 11000 K synthetic model with vsini of 40 km/s CLEAR MINIMUM EXISTS

10. Chi-Square Map HD 17081 Using chi-square map, can estimate uncertainties. Using 3 parameter fitting space, chi- square difference of 21.1 represents a formal 99.99% confidence level. closest model has greater than 2300 chi-square difference

11. Theoretical Results Investigated SNR vsini varying Fe abundance random noise to log(gf) values micro/macro turbulence binaries normalization conclusion other than binaries, for reasonable variations, ~100-200 K uncertainties

12. Results Name Metallic Lines (Teff, Log(g)) Reduced χ 2 Hγ (Teff, Log(g)) Hβ (Teff, Log(g)) LiteratureValue HD 142666 7700, 4.0 6.68 7300, 3.5 7500, 4.5 8500 HD 144432 7700, 4.0 7.16 7200, 3.0 7400, 4.5 7950 HD 17081 13700, 4.0 6.21 11800, 3.5 12000, 4.0 12300 HD 244604 8600, 3.5 5.29 8200, 4.0 8100, 4.0 ~9500 HD 31648 8200, 3.5 25.77 8300, 4.0 8700 ± 1005 HD 34282 10200, 4.5 2.23 9800, 4.5 10100, 4.5 8700 +410/- 198 HD 35187 9200, 4.0 4.66 8700, 4.0 8600, 4.0 9100 ± 420 HD 36112 7900, 4.0 5.36 8000, 5.0 8100, 5.0 7750 ± 358 HD 53367 31200, 4.5 3.61 29200, 4.0 31400, 4.0 31600 ± 3650

13. HD 17081 B7IV Classification Best Fit 13700 K Log(g)=4.0 vsini=20 km/s Literature Results ~12300 K

14. HD 17081

16. HD 17081 Balmer Fits Best Fit: 11800 K, Log(g)=3.5Best Fit: 12000 K, Log(g)=4.0

17. HD 34282 A0e+sh Classification Best Fit 10200 K Log(g)=4.5 vsini=108 km/s Literature Results ~8700 (+410,- 198) K

18. HD 34282

19. HD 34282 Balmer Fits Best Fit: 9800 K, Log(g)=4.5Best Fit: 10100 K, Log(g)=4.5

20. HD 36112 A8e Classification Best Fit 7900 K Log(g)=4.0 vsini=52 km/s Literature Results ~7700 K

21. HD 36112

22. HD 36112 Balmer Fits Best Fit: 8000 K, Log(g)=5.0Best Fit: 8100 K, Log(g)=5.0

23. HD 31648 A3pshe+ Classification Best Fit 8200 K Log(g)=3.5 vsini=95 km/s Literature Results 8700 K 9250 K, Log(g)=3.5

24. HD 31648

25. Difficult Stars: BF Ori A5II-IIIe var Best Fit ~7500 Log(g)~4.0 vsini~53 km/s Literature Results 6750

26. BF Ori

27. HR DIAGRAM: New Temperatures

28. HR Diagram: New Temperatures and Distances

29. HR Diagram: New Temperatures and Computed Photometry

30. FUTURE WORK Automated fitting for all field HAeBe stars with ESPaDOnS observations. Automated fitting for all field HAeBe stars with ESPaDOnS observations. Use improved temperatures to improve mass and age estimates. Use improved temperatures to improve mass and age estimates. Use Bayesian statistical approach to improving luminosities. Use Bayesian statistical approach to improving luminosities. Major Issues Abundances for chemically peculiar stars. Abundances for chemically peculiar stars. Micro/macro turbulence. Micro/macro turbulence. Systematic normalization issues. Systematic normalization issues.

31. THE END

32. Balmer Line Normalization: HD36112

33. Balmer Line Normalization: HD139614

34. Balmer Line Normalization: Comparison between ESPaDOnS and FORS1 HD 36112

35. Uncertainty vs SNR For 15000 K synthetic model with 40 km/s vsini.

36. 15000 K synthetic model with 40 km/s vsini

37. Difficult stars: HD 31293

38. HD 31293