Automated Fitting of High-Resolution Spectra of HAeBe stars - PowerPoint PPT Presentation

1 / 38
About This Presentation
Title:

Automated Fitting of High-Resolution Spectra of HAeBe stars

Description:

Automated Fitting of High-Resolution Spectra of HAeBe stars. Improving fundamental parameters ... Take advantage of high-res ESPaDOnS spectra. Minimal ... – PowerPoint PPT presentation

Number of Views:28
Avg rating:3.0/5.0
Slides: 39
Provided by: jas576
Category:

less

Transcript and Presenter's Notes

Title: Automated Fitting of High-Resolution Spectra of HAeBe stars


1
Automated Fitting of High-Resolution Spectra of
HAeBe stars
  • Improving fundamental parameters
  • Jason Grunhut
  • Queens University/RMC

2
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

3
Full ESPaDOnS Spectral Range 11000 K synthetic
model
4
Full ESPaDOnS Spectral Range 11000 K synthetic
model
5
Spectrum variation with temperature from nearest
Kurucz models (500 K)
6
Spectrum variation with temperature from nearest
Kurucz models (500 K)
7
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.

8
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.

9
Theoretical Results for 11000 K synthetic model
with vsini of 40 km/s
CLEAR MINIMUM EXISTS
10
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

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 ?2 H? (Teff, Log(g)) Hß (Teff, Log(g)) Literature Value
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 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
  • vsini20 km/s
  • Literature Results
  • 12300 K

14
HD 17081
15
HD 17081
16
HD 17081 Balmer Fits
Best Fit 11800 K, Log(g)3.5
Best Fit 12000 K, Log(g)4.0
17
HD 34282
  • A0esh Classification
  • Best Fit
  • 10200 K
  • Log(g)4.5
  • vsini108 km/s
  • Literature Results
  • 8700 (410,-198) K

18
HD 34282
19
HD 34282 Balmer Fits
Best Fit 9800 K, Log(g)4.5
Best Fit 10100 K, Log(g)4.5
20
HD 36112
  • A8e Classification
  • Best Fit
  • 7900 K
  • Log(g)4.0
  • vsini52 km/s
  • Literature Results
  • 7700 K

21
HD 36112
22
HD 36112 Balmer Fits
Best Fit 8000 K, Log(g)5.0
Best Fit 8100 K, Log(g)5.0
23
HD 31648
  • A3pshe Classification
  • Best Fit
  • 8200 K
  • Log(g)3.5
  • vsini95 km/s
  • Literature Results
  • 8700 K
  • 9250 K, Log(g)3.5

24
HD 31648
25
Difficult StarsBF Ori
  • A5II-IIIe var
  • Best Fit
  • 7500
  • Log(g)4.0
  • vsini53 km/s
  • Literature Results
  • 6750

26
BF Ori
27
HR DIAGRAMNew Temperatures
28
HR DiagramNew Temperatures and Distances
29
HR DiagramNew Temperatures and Computed
Photometry
30
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.

31
THE END
32
Balmer Line Normalization HD36112
33
Balmer Line Normalization HD139614
34
Balmer Line NormalizationComparison between
ESPaDOnS and FORS1
HD 36112
35
Uncertainty vs SNRFor 15000 K synthetic model
with 40 km/s vsini.
36
15000 K synthetic model with 40 km/s vsini
37
Difficult starsHD 31293
38
HD 31293
Write a Comment
User Comments (0)
About PowerShow.com