New Insight Into the Dust Content of Galaxies Based on the Analysis of the Optical Attenuation Curve.

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New Insight Into the Dust Content of Galaxies Based on the Analysis of the Optical Attenuation Curve

Dust Dust is an important component of a galaxy In situ, it affects the evolution of the galaxy by the role it plays in the process of star formation Observationally, it shapes the spectra through absorption and scattering of ultraviolet and optical photons and emission in the mid- and far-infrared In a typical star-forming galaxy, almost 1/3 of the total luminosity is processed by dust into the IR

How to quantify dust attenuation? In the case of a single point source (star, AGN) behind a dust Screen, attenuation is termed “extinction” In the case of a whole galaxy, the term attenuation refers to the overall absorption (scattering) of photons emitted in all directions by stars in all location within the galaxy. It can always be written as:

To determine the dust attenuation of SDSS galaxies, we followed the prescription of Calzetti et al. 1994

- analyzed 39 starburst galaxies - as a measure of dust attenuation she used Balmer optical depth in H  and H  lines defined as: - divided spectra into 6  l B bins - averaged spectra in each bin (to average out the effect of different stellar population ages) - divided all spectra with spectrum that corresponds to the smallest  l B (pair method) - at the end, averaged all spectra to get attenuation curve Derivation of the Attenuation Law of Starburst Galaxies (Calzetti et al. 1994)

This Work Similar method (Calzetti et al., 1994) was applied in this work. It was done with real spectra and modeled spectra 1. Observed spectra (Christy Tremonti & Tim Heckman) - used ~10000 high quality (S/N>5) galaxy spectra from DR2 database of the SDSS catalog - AGNs were selected out 2. Modeled spectra (myself) - used ~ 5*10 5 stochastically generated galaxy spectra

Method of Selection It is known that dust has the same effect on spectra as 1. Age/SFH 2. Metallicity 3. IMF Since “pair method” is based on comparison of same-type objects we must break down (or at least to reduce) all these degeneracies with careful selection of galaxies

Method of Selection I D n 4000 measures the 4000 A – break good tracer of the mean stellar age H  A measure the absorption in Balmer line good tracer of recent burst of star formation The selection was performed in the D n 4000-H  A space. Spectra were divided into 10 boxes in the D n 4000-H  A space (Same scales for comparison)

Binning & Averaging Real spectra Modeled spectra Spectra IN EACH BIN were divided into 7 Balmer optical depth bins in line ( ) and averaged. Example for Bin 2

Rescaled Optical Depth (Q n ) is calculated F n and F 1 are spectra in n th and 1 st bin H  and H  are luminosities in lines Interesting to note this bump in both (observation & model) cases near 4000A In higher Balmer optical depths

Dividing & Smoothing All spectra were divided with the spectrum that corresponds to smallest Balmer optical depth (nearly dust-free) and smoothed on 200 A scale Real spectraModeled spectra

Effective Attenuation Law The effective attenuation curve (Q eff ) i.e. the weighted (with ) average of Q n for each box is calculated. Real spectra Modeled spectra

Reddening The reddening experienced by old stars relative to young stars i.e  c B /  l B for all 10 boxes. Modeled spectra Real spectra

Attenuation curve The Q eff /(Q eff (H  )-Q eff (H  ) is calculated observationmodel

Conclusion 1.Dividing spectra into boxes in the D n 4000-H  A space enable us to derive the corresponding attenuation curves and to correct the individual spectra for dust attenuation 2.The remarkable similarity between real and modeled spectra enable us to analyze dust properties of galaxies in more detail

Future work This analysis, in the case of observed spectra, was Performed using DR2 SDSS database. We plan to do it with DR4 Database also. We expect to improve the analysis in two way: - boxes in the D n 4000-H  A space will be more populated with objects even for large D n 4000 values - if the number of objects in each box is large enough, we plan to bin them according to metallicities also