toward 3d spectra of galaxies
DESCRIPTION
Toward 3D Spectra of Galaxies. Ching-Wa Yip Johns Hopkins University. Collaborators. Alex Szalay (JHU) Rosemary Wyse (JHU) László Dobos (ELTE) Tamás Budavári (JHU) Istvan Csabai (ELTE). Question: how does a galaxy look like from various viewing angles?. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/1.jpg)
Toward 3D Spectra of Galaxies
Ching-Wa YipJohns Hopkins University
![Page 2: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/2.jpg)
Alex Szalay (JHU) Rosemary Wyse (JHU) László Dobos (ELTE) Tamás Budavári (JHU) Istvan Csabai (ELTE)
Collaborators
![Page 3: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/3.jpg)
We only see one side of a given galaxy◦ orbital period of Sun ~250 million years
Large sky surveys (SDSS; LAMOST) provide 106/107 galaxy spectra◦ Spectroscopic fiber projects fixed (3”) diameter
area on the sky◦ Galaxies are inclined at various angles; and
located at various distances In principal we can statistically extract
spatial information from the SDSS spectra
Question: how does a galaxy look like from various viewing angles?
![Page 4: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/4.jpg)
Comparison with Integral Field Spectroscopy (sometimes called “3D”)
Ealet et al. 2008
x
y
fλ(x, y, λ)
![Page 5: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/5.jpg)
3D Spectrum fλ(x, y, z, λ)
rq
f
Line of sight
![Page 6: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/6.jpg)
Galaxies are at Different Inclinations
(from SDSS Image Cutout tool)
![Page 7: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/7.jpg)
Inclination Dependency of Average Spectrum
Face-on galaxies
Edge-on galaxies
![Page 8: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/8.jpg)
Inclination-Dependent Extinction in Stellar Continuum
Yip et al. 2009 submitted
![Page 9: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/9.jpg)
Optical Thickness of Stellar Continuum of Galaxies
Screen model Slab model
Sandwich model
Best-fit theoretical model is the slab model.Best-fit face-on extinction ~ 0.2 mag (SDSS g band).
θ
![Page 10: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/10.jpg)
Inclination Dependency of Extinction in HII Region (singly- ionized hydrogen)
•Balmer decrement does not show inclination dependency.•HII region vs. Continuum-generating stars: • Different dust configuration and/or mechanism.
Theoretical, no dust value
![Page 11: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/11.jpg)
Overall model•Uniform mix of dust/stars•HII region outside of the mix
(not to scale)
O/B stars give arise to HII region: Balmer series hydrogenlines; [OIII] lines etc.
Low-mass stars give rise to stellar continuum
Line of sight
![Page 12: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/12.jpg)
N-D hypercube Multi-linear interpolation to achieve
arbitrary computational resolution in parameters
Galaxy Composition through N-D Parameter Estimation
(x0, y0) (x1, y1)
(x2, y2)(x3, y3)
Length = 1 (x, y)
2-D:f(x, y) = f(x0, y0) * (dx0 * dy0) + f(x1, y1) * (dx1 * dy1) + f(x2, y2) * (dx2 * dy2) + f(x3, y3) * (dx3 * dy3)
N-D:f(x) = ∑ f(zi) ∏ (1 – |xj - zij| )
where zi are the neighboring parameter points
dx0
dy0
.
![Page 13: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/13.jpg)
N-D Parameter Estimation on SDSS Spectra
For 4D model (age, metallicity, star-forming time scale, extinction)• ~300,000 model spectra are generated on the fly• ~30 minutes (high resolution model + SDSS spectrum, 70km/s) • parameter uncertainties are estimated on object-to-object basis
![Page 14: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/14.jpg)
Stellar population and dust properties as a function of galaxy radius
Results support inside-out growth (Yip & Wyse 2009 in prep.)
Similar to tree-ring studies
N-D Parameter Estimation on Photometry
![Page 15: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/15.jpg)
Stellar population and dust properties as a function of galaxy radius
Results support inside-out growth (Yip & Wyse 2009 in prep.)
Similar to tree-ring studies
N-D Parameter Estimation on Photometry
![Page 16: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/16.jpg)
Spectrum as a function of galaxy radius◦ Galaxies from large surveys are located at various
distances◦ Need to ensure we are comparing apples to apples
Spectrum as a function of φ angle◦ Need average spectrum for spiral arm, bar etc.◦ Galaxy Zoo images/results will be useful
Spectral analyses in 3D◦ Classification of galaxies◦ Diversity in galaxies◦ Composition modeling for stellar populations and dust
Challenges Toward 3D Spectra
![Page 17: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/17.jpg)
Fast Parameter Estimation◦ We would like to understand how spectra are mapped
to the parameters Data compression techniques (e.g., PCA) to reduce the
dimensionality of spectra (Szalay) Use Catastrophe theory (Szalay)
◦ Parameter degeneracy is present (e.g., age-metallicity degeneracy)
Dealing with 105 or more model spectra Store them in database to alleviate the need for
interpolations Difficulty: generality for complex models Some spectral components (stellar bursts) are additive
Challenges: Galaxy Composition
![Page 18: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/18.jpg)
How are spectra mapped to physical parameters
Yip & Szalay in progress
Principal Component Analysis (PCA) on model spectra to reduce their dimensionality
Expansion coefficients vs. parameters tell us about parameter degeneracy
![Page 19: Toward 3D Spectra of Galaxies](https://reader035.vdocuments.net/reader035/viewer/2022062521/56816768550346895ddc4f0b/html5/thumbnails/19.jpg)
We have derived empirical galaxy spectrum as a function of inclination (θ), averaged over φ and projected radius < 0.5 half-light radius.
We will derive empirical galaxy spectrum as a function of both the radius (r) and azimuth angle (φ).
We have developed a N-D parameter estimation code for calculating the composition of galaxies (stars, gas & dust).
Summary