The cosmic star formation rate: a theoretical approach

Astronomy and Astrophysics – Astrophysics – Cosmology and Extragalactic Astrophysics

Scientific paper

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113 pages, 33 figures, 5 tables, Master degree thesis

Scientific paper

The cosmic star formation rate (CSFR), namely the star formation rate in a unitary comoving volume of the Universe, is a fundamental clue to investigate the history of the assembling and evolution of structures in the Universe. Here we develop a method to study the CSFR from a purely theoretical point of view. Starting from detailed models of chemical evolution, which best fit the properties of local galaxies, we obtain the histories of star formation of galaxies of different morphological types (ellipticals, spirals, irregulars). These histories are then used to determine the photometric evolution of galaxies by means of a spectro-photometric code. The next step in computing the CSFR is the calculation of the luminosity density for which we need to know how galaxies are distributed in the Universe at any redshift. This is possible thanks to the luminosity function (LF) of galaxies. At this point we calculate the CSFR under different evolutionary scenarios. First, we study the hypothesis of a pure luminosity evolution scenario, in which galaxies are supposed to form all at the same redshift and then evolve only in luminosity without any merging or interaction. In other words, we assume no number density evolution. Then we define scenarios in which the number density and the slope of the LF are assumed to vary with redshift. All our results have been compared with data available in literature. We conclude that a pure number density evolution does not provide a good fit to the data. On the other hand, a variation in the number density for ellipticals and spirals as a function of redshift can provide a better fit to the observed CSFR, although the data at very high redshift are still quite uncertain and this solution predicts that most of the metals in the Universe have been produced by spirals, a prediction which still needs to be tested.

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