Stellar evolution and chemical enrichment
Chemical evolution is a natural outcome of galaxy evolution in cosmological hydrodynamical simulations. Models of chemical evolution have been included in cosmological simulations of cosmic structure formation to properly address the study of the chemical enrichment process: the distribution of metals in the ISM and in the circum-galactic medium (CGM) encodes valuable information of the past history of star formation and feedback, and is a crucial feature of hydrodynamical simulations (e.g. [173] ,[174] ). At variance with observations that explicitly resolve individual stars, cosmological simulations provide a coarse sampling of stellar populations by means of star particles. The majority of state-of-the-art cosmological hydrodynamical simulations of both large cosmological volumes and individual galaxies have star particles whose mass typically ranges between $10^8$ and $10^3$ M$_{\odot}$ according to resolution (e.g. [36] ,[40] ,[39] ,[175] ,[41] ,[176] ,[139] ,[6] ,[177] ,[178] ,[179] ,[180] ,[155] ,[135] ,[181] ). In these simulations, star particles are resolution elements: each of them represents a simple stellar population (SSP), i.e. an ensemble of coeval stars that share the same initial metallicity. Every stellar particle initially shares the chemical composition of the gas element from which it has been originated, and is characterized by an initial mass function (IMF).
The IMF $\phi (m)$ determines the number of stars per unit mass interval: $$ \phi (m) = \beta m^{- \alpha} \,\,\,,
$$ within a given mass range $[M_{inf}, M_{sup}]$. The coefficient $\alpha$ sets the slope of the power law over the mass range or in the different mass intervals within the mass range. For each mass interval, a normalization constant $\beta$ is computed by imposing that $\int m \, \phi (m) \, dm = 1$ over the global mass range and continuity at the edges of subsequent mass intervals.
Commonly assumed IMFs consist of single- [182] ,[183] or multiple-slope [184] ,[185] ,[186] power law, but more sophisticated shapes have been proposed (e.g. [187] ). We refer the reader to [188] ,[189] ,[190] ,[191] ,[192] ,[193] ,[164] for details about different IMFs and to appreciate how adopting different IMFs can have an impact on metal distribution on cosmological simulations.
The IMF directly regulates the relative ratio between stars of different initial mass, thus affecting the relative abundance of elements contributed by different types of stars. Stellar evolution predicts that all stars with masses larger than $M_{up}=8$ M$_\odot$ end their life as core collapse or type-II supernovae (SNe II) (see [194] and references therein). Under this assumption, the total amount of energy (typically $10^{51}$ erg per SN) that each star particle can release in the surrounding gas can be calculated. Within the approximation that the typical lifetime of massive stars which explode as SNII does not exceed the typical time step of the simulation, feedback energy and metals are injected by the dying star in the so-called ``instantaneous recycling approximation'', i.e. in the same time step. On the other hand, type-Ia SNe (SNe Ia) are believed to arise from the thermonuclear explosion of white dwarfs (see [195] and references therein). They lead to an explosion which is significantly delayed with respect to the time of creation of the star particle, as the white dwarf in a binary system has to accrete matter from the companion and reach the mass threshold for the onset of thermonuclear burning. Stars in the asymptotic giant branch (AGB) experience significant stellar mass loss and contribute significantly to the nucleosynthesis of heavy elements (see [196] and references therein).
Cosmological hydrodynamical simulations which feature an accurate model for stellar evolution and chemical enrichment can evaluate the number of stars aging and eventually exploding as SNe, as well as the amount of metals polluting the surrounding ISM. Besides assuming an IMF, commonly adopted models (e.g. [172] ) include stellar lifetimes, delay functions and table of stellar yields to calculate the chemo-energetic imprint mainly left by SNe Ia, SNe II, and AGB stars.
Chemical evolution models account for the evolutionary timescales of stars with different masses by adopting mass-dependent lifetime functions (i.e. functions that describe the age at which a star of mass $m$ dies, e.g. [197] ,[198] ,[199] ). Stars with an initial mass larger than $M_{up}$ and lower than $M_{sup, SNII}=40$ $M_\odot$ are assumed to end their life exploding as core-collapse SNe, while stars that are more massive than $M_{sup, SNII}$ are assumed to implode in BHs directly, and thus do not contribute to further chemical enrichment or stellar feedback energy.
A fraction (usually constant) of stars relative to the whole mass range is assumed to be located in binary systems that are progenitors of SNe Ia. Assuming a stellar evolution scenario for SNe Ia (e.g. according to the model by [200] ) and adopting lifetime functions and delay time distribution functions, the rate of SN Ia explosions can be computed.
The production of different heavy metals by stars that evolve and eventually explode is followed by adopting stellar yields. They represent the ejected mass of different metal species $i$ produced by a star of mass $m$ and initial metallicity $Z$, i.e. $p_{Z_{i}}(m,Z)$. In general, predictions for at least three main processes are needed: the continuous mass loss of AGB stars, SNe II and SNe Ia. Tables of mass- and metallicity-dependent stellar yields are incorporated in the chemical evolution models of state-of-the-art cosmological hydrodynamical simulations. Predictions for stellar yield are still affected by uncertainties, mainly due to the poorly understood process of mass loss through stellar winds in stellar evolution models. Commonly adopted sets of stellar yields include: [201] for SNe Ia, mass- and metallicity-dependent yields by [202] ,[203] ,[204] for AGB stars, mass- and metallicity-dependent yields by [205] ,[206] ,[207] ,[208] for SNe II. We refer the reader to (e.g. [209] ,[210] ) for a comprehensive review of the analytic formalism.
As a final caveat, we note that comparisons of results from simulations to observations of single stars are usually performed under the assumption that e.g., the metal content of a star particle in simulations statistically reproduces the mean metallicity of the SSP that the star particle samples. More accurate techniques are introduced in [211] ,[212] .
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