Absorption and emission in the ISM
In this thesis, I am interested in computing in a physically consistent way the combined in uence of nebular emission and interstellar absorption on the ultraviolet spectra of star-forming galaxies. I achieve this through an idealized description of the main features of the ISM (inspired from Fig. 1.5) and by appealing to a combination of the photoionization code cloudy (version 13.3; Ferland et al. 2013a) with the spectral synthesis code synspec (Hubeny & Lanz, 1995), which allows the computation of interstellar-line strengths based on the ionization structure solved by cloudy (in practice, this combination is performed via the program cloudspec of Hubeny et al. 2000; see also Heap et al. 2001). I describe this approach in Chapter 4. To this end, it is useful here to brie y review the main features of the cloudy and synspec codes.
Photoionization code: CLOUDY
The aim of photoionization codes is to simulate the physical conditions describing the ionization structure of ionized nebulae. In general, codes of this kind include photoionization, recombination, free-free radiation, collisional excitation, collisional ionization and charge exchange reactions. Most codes use static photoionization solvers, which assume that the gas is in both ionization and thermal equilibrium (although some codes are built to study nebulae not in equilibrium; see Tylenda 1979; Marten & Schoenberner 1991). The basic equations on which a photoionization code relies to compute a model are the radiative transfer equation, the ion equilibrium equation and energy conservation.
In this work, I use the cloudy code (Ferland et al., 1998, 2013b) to compute the physical conditions in Hii and Hi regions around young stars. This is one of the most widely used photoionization codes, although other codes have been proposed, among which nebu (Pequignot et al., 1978), ion (Netzer & Ferland, 1984) and mappings (Sutherland & Dopita, 1993). These codes dier mainly in their approach to compute the transfer of ionizing photons, in particular to describe: the on the spot reabsorption, where the photons are reabsorbed near the location at which they were emitted; the outward-only approximation, which adds the locally-produced diuse radiation to the incident ux and transfers this in dierent directions; and the full treatment, whereby the inward and outward diuse radiation are computed in a single averaged direction. Also, some codes include Monte-Carlo techniques to solve the transfer of radiation (Och et al., 1998). Another dierence we can nd between dierent codes is in the treatment of geometry. Most codes assume spherical or plane-parallel 1D geometries. However, some other codes are also built in 3D, which increases the number of free parameters, complicating the modeling but treating the distribution of densities in the cloud in a more realistic way (Gruenwald et al., 1997; Morisset, 2006). It is also important for photoionization codes to keep their atomic databases up to date, as measurements of dierent processes of atomic physics are improved constantly. Finally, we note that several codes were designed to model shocks induced by winds of ionizing stars, which requires to treat simultaneously hydrodynamic and microphysics processes (Schmidt-Voigt & Koeppen, 1987; Marten & Schoenberner, 1991; Frank & Mellema, 1994; Mellema & Frank, 1995; Mellema, 1995; Rodriguez-Gaspar & Tenorio-Tagle, 1998). The latest version of the cloudy code I adopt in this work (Ferland et al., 2013b) presents several improvements over previous versions, in particular, in the techniques used to compute dusty molecular environments, the ionization and chemistry solvers and also the treatment of atomic data (Ferland et al., 2013b). cloudy is a microphysics code. Its goal is to simulate physical conditions in clouds covering wide ranges of temperature and density. It calculates in a self-consistent way the physical conditions (distributions of temperature, density and ionization) and the resulting spectrum across a cloud ionized by an input radiation eld. This is achieved by simultaneously solving the equations of statistical and thermal equilibrium, which balance ionization-neutralization processes and heating-cooling processes, respectively. I am interested in the emission and absorption spectra of the Hii and Hi regions surrounding young stellar populations in galaxies. The parameters I use for this modeling are described in Section 4.1.1.
The output of a cloudy calculation includes the predicted thermal, ionization, and chemical structures of the cloud. To compute the emergent spectrum, cloudy divides the cloud into a set of thin concentric shells chosen to have thicknesses small enough for the physical conditions across them to be nearly constant. In each of these layers, the transmitted radiation eld is the net emission emerging from the shielded face of the cloud. It includes both the attenuated incident and the diuse radiation elds. This is summarized in Fig. 2.4, which displays the scheme of a cloudy calculation. I assume spherical geometry in my calculations. In this case, cloudy takes into account ionization by the diuse continua and lines produced on the far side of the nebula (beyond the central object) and does not attenuate the ionizing continuum by pure scattering opacities, such as electron scattering, back scattering by grains and Rayleigh scattering.
General spectrum synthesis code: SYNSPEC
synspec is a general spectrum synthesis code (Hubeny & Lanz, 2011). Other examples of spectrum synthesis codes are spectrum (Gray & Corbally, 1994) and synth3 (which includes a molecular equilibrium solver; Kochukhov 2007). When used as a standalone program, synspec takes as input a model atmosphere, such as tlusty (Hubeny, 1988; Lanz & Hubeny, 2003) or kurucz (Kurucz, 1992). Then, it reads a comprehensive line list and selects the lines contributing to the total opacity, based on the physical parameters of the model atmosphere. Finally, synspec solves the radiative transfer equation in a specied wavelength range at a given spectral resolution. When synspec is called from cloudspec, the model atmosphere used is that of tlusty and the lines contributing to the opacity are calculated previously with cloudy.
For the purpose of this thesis, I call synspec from cloudspec and use as input a stellar population spectrum from galaxev. I compute the absorption at wavelengths between 1200 and 4000A. The code also allows one to convolve absorption-line proles with a user-supplied velocity eld. In Chapter 4, I describe the way in which I compute nebular emission and ISM absorption with these tools. I also mention in Chapter 7 ongoing and future work aiming to study the impact of velocity elds on ultraviolet interstellar absorption features.
The star formation history of galaxies
The appearance of a galaxy at a given time results from a combination of dierent physical processes acting on dierent timescales, such as star formation, chemical enrichment of the ISM, mergers and AGN feedback. All these processes are related to the Star Formation History of the galaxy. The SFH is therefore an important ingredient of the interpretation of observed galaxy spectra using spectral evolution models. Some popular analytic functions include:
Constant star formation rate, (t) =const.
Exponentially declining star formation rate: (t) = exp(−t/ ), where is the star formation timescale.
Delayed star formation rate: (t) = t exp(−t/ ).
SFHs in spectral analyses may also be taken from sophisticated simulations of galaxy formation (e.g., Pacici et al., 2012). According to Eq. 2.2, we can describe the spectral evolution of a galaxy with the spectral evolution of simple stellar populations. As an example, we show in Fig. 2.6 the spectrum of a simple stellar population at dierent ages and solar metallicity computed with galaxev. The plot shows how at early ages the emission of young and massive
stars dominates the ultraviolet region and how as the population evolves and these stars leave the MS, the peak of emission moves towards the optical and near infrared. After a few Gyr, the emission from hot post-AGB stars makes the ultraviolet emission rise again and remain roughly constant at greater ages.
Ultraviolet signatures of young stellar populations
In this Section, I start by describing the main features of the stellar population synthesis code I adopt to compute ultraviolet spectral signatures of young stellar populations (Section 3.2.1). Then, I brie y review the main properties of the Fanelli et al. (1992) ultraviolet spectral indices (Section 3.2.2). I examine the dependence of index strengths on age, metallicity and integrated stellar mass for simple stellar populations (Section 3.2.3), along with the dependence of ultraviolet, optical and near-infrared broadband magnitudes on integrated stellar mass (Section 3.2.4).
Stellar population synthesis modelling
I adopt the latest version of the Bruzual & Charlot (2003) stellar population synthesis code (Charlot & Bruzual, in preparation; see also Woord et al. 2016) to compute emission from stellar populations of ages between 104 yr and 13.8Gyr at wavelengths between 5.6A and 3.6 cm, for metallicities in the range 0.0001 Z 0.04 (assuming scaled-solar heavy-element abundance ratios at all metallicities). This version of the code incorporates updated stellar evolutionary tracks computed with the PARSEC code of Bressan et al. (2012) for stars with initial masses up to 350 M (Chen et al., 2015), as well as the recent prescription by Marigo et al. (2013) for the evolution of thermally pulsing AGB stars (see Section 2.1.2). The present-day solar metallicity in these calculations is taken to be Z = 0.01524 (the zero-age main sequence solar metallicity being Z0 = 0.01774; see Bressan et al. 2012). Note that the inclusion of very low-metallicity, massive stars is important to investigate the properties of primordial stellar populations (Bromm & Yoshida, 2011). These evolutionary tracks are combined with various stellar spectral libraries to describe the properties of stars of dierent eective temperatures, luminosities, surface gravities, metallicities and mass-loss rates in the Hertzsprung-Russell diagram (see Section 2.1.4). For the most part (see adjustments below), the spectra of O stars hotter than 27,500K and B stars hotter than 15,000K are taken from the TLUSTY grid of metal line-blanketed, non-local thermodynamic equilibrium (non-LTE), plane-parallel, hydrostatic models of Hubeny & Lanz (1995); Lanz & Hubeny (2003, 2007). The spectra of cooler stars are taken from the library of line-blanketed, LTE, plane-parallel, hydrostatic models of Martins et al. (2005), extended at wavelengths shorter than 3000A using similar models from the UVBLUE library of Rodrguez-Merino et al. (2005). At wavelengths in the range 3525 . . 7500Å, the spectra of stars with eective temperatures in the range 2800 . Teff . 36, 000K are taken from the observational MILES library of Sanchez-Blazquez et al. (2006). At wavelengths in the range 900 . . 3000Å, the spectra of MS stars with eective temperatures in the range 17, 000 . Teff . 60, 000K are taken from the theoretical library of Leitherer et al. (2010), computed using the WM-basic code of Pauldrach et al. (2001) for line-blanketed, non-LTE, spherically extended models including radiation driven winds. Finally, for Wolf-Rayet stars, the spectra are taken from the (high-resolution version of the) PoWR Chapter 3. Calibration of the SSP models in the ultraviolet library of line-blanketed, non-LTE, spherically expanding models of Hamann & Grafener (2004, see also Grafener et al. 2002; Hamann & Grafener 2003; Hamann et al. 2006; Sander et al. 2012; Hainich et al. 2014). The inclusion of the Leitherer et al. (2010) and Hamann & Grafener (2004) spectral libraries enables the modelling of P-Cygni line proles originating from winds of massive OB and Wolf-Rayet stars in the integrated ultraviolet spectra of young stellar populations (for example, for the Nv 1240, Si iv 1400 and Civ 1550 lines; e.g. Walborn & Panek 1984). For completeness, the spectra of the much fainter, hot post-AGB stars are taken from the library of line-blanketed, non-LTE, plane-parallel, hydrostatic models of Rauch (2002).
Table of contents :
1.1 Historical context
1.2 Early epochs of the Universe
1.3 Galaxy types
1.4 Components of a Galaxy
1.4.1 Classication of stars
1.4.2 The ISM: components and phases
1.4.3 Physical processes in the ISM
1.5 Chemical content of a galaxy
1.6 Galaxy spectral energy distributions
1.7 Future observing facilities
2 Modeling spectral energy distributions of galaxies
2.1 Stellar emission
2.1.1 Stellar population synthesis codes
2.1.2 Evolutionary tracks
2.1.4 Stellar spectral libraries
2.2 Absorption and emission in the ISM
2.2.1 Photoionization code: CLOUDY
2.2.2 General spectrum synthesis code: SYNSPEC
2.2.3 Summary of the modeling
2.3 The star formation history of galaxies
3 Calibration of the SSP models in the ultraviolet
3.2 Ultraviolet signatures of young stellar populations
3.2.1 Stellar population synthesis modelling
3.2.2 Ultraviolet spectral indices
3.2.3 Dependence of index strength on age, metallicity and integrated stellar mass
3.2.4 Associated broadband magnitudes
3.3 Interpretation of ultraviolet star-cluster spectroscopy
3.3.1 Observational sample
3.3.2 Model library
3.3.3 Age, metallicity and stellar-mass estimates
4 Inuence of the ISM on ultraviolet spectra of star-forming galaxies
4.1 ISM modelling
4.1.2 Examples of model spectra
4.2 Ultraviolet tracers of stars and the ISM
4.2.1 Features tracing young stars
4.2.2 Features tracing nebular emission
4.2.3 Features tracing interstellar absorption
4.2.4 Important composite features
5 On statistical tools for galaxy SED-tting
5.1 Standard methodology
5.2 In this work: BEAGLE.
6 Results from galaxy SED-tting with BEAGLE
6.1 Ly and Ciii] emission in z = 7 − 9 galaxies: accelerated reionization around luminous star-forming systems?
6.2 The three most metal-poor nearby galaxies
6.3 10 nearby galaxies with hard optical spectra
7 Conclusions and perspectives
A Ultraviolet stellar absorption-line indices
B Correction of ultraviolet index strengths for Galactic absorption
C P-Cygni proles