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Automated Fitting of High-Resolution Spectra of HAeBe stars. Improving fundamental parameters Jason Grunhut Queen’s University/RMC. Motivation. Common ways to determine temperature Photometry SED Problems Extinction/emission and calibrations Many corrections necessary
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Automated Fitting of High-Resolution Spectra of HAeBe stars Improving fundamental parameters Jason Grunhut Queen’s University/RMC
Motivation • Common ways to determine temperature • Photometry • SED • Problems • Extinction/emission and calibrations • Many corrections necessary • Take advantage of high-res ESPaDOnS spectra • Minimal corrections required
Spectrum variation with temperature from nearest Kurucz models (±500 K)
Spectrum variation with temperature from nearest Kurucz models (±500 K)
Automated Fitting of Spectra • Search through a pre-defined grid of synthetic spectra. • 4200-5200 Angstroms • Solar abundances. • Most current VALD line list. • Micro-turbulent velocity of 2 km/s. • No macro-turbulence. • Models computed using synth3 • 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.
How Program Works • 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). • Model with minimum chi-square represents best fit. • Radial velocity is fit for a final time for best model.
Theoretical Results for 11000 K synthetic model with vsini of 40 km/s CLEAR MINIMUM EXISTS
Chi-Square MapHD 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
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
HD 17081 • B7IV Classification • Best Fit • 13700 K • Log(g)=4.0 • vsini=20 km/s • Literature Results • ~12300 K
HD 17081 Balmer Fits Best Fit: 11800 K, Log(g)=3.5 Best Fit: 12000 K, Log(g)=4.0
HD 34282 • A0e+sh Classification • Best Fit • 10200 K • Log(g)=4.5 • vsini=108 km/s • Literature Results • ~8700 (+410,-198) K
HD 34282 Balmer Fits Best Fit: 9800 K, Log(g)=4.5 Best Fit: 10100 K, Log(g)=4.5
HD 36112 • A8e Classification • Best Fit • 7900 K • Log(g)=4.0 • vsini=52 km/s • Literature Results • ~7700 K
HD 36112 Balmer Fits Best Fit: 8000 K, Log(g)=5.0 Best Fit: 8100 K, Log(g)=5.0
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
Difficult Stars:BF Ori • A5II-IIIe var • Best Fit • ~7500 • Log(g)~4.0 • vsini~53 km/s • Literature Results • 6750
FUTURE WORK • Automated fitting for all field HAeBe stars with ESPaDOnS observations. • Use improved temperatures to improve mass and age estimates. • Use Bayesian statistical approach to improving luminosities. Major Issues • Abundances for chemically peculiar stars. • Micro/macro turbulence. • Systematic normalization issues.
Balmer Line Normalization:Comparison between ESPaDOnS and FORS1 HD 36112
Uncertainty vs SNRFor 15000 K synthetic model with 40 km/s vsini.