* Astronomy

Title: Dynamical population synthesis: Constructing the stellar single and binary contents of galactic field populations
Authors: Michael Marks, Pavel Kroupa

The galactic field's late-type stellar single and binary population is calculated on the supposition that all stars form as binaries in embedded star clusters. A recently developed tool (Marks, Kroupa & Oh) is used to evolve the binary star distributions in star clusters for a few Myr so that a particular mixture of single and binary stars is achieved. On cluster dissolution the population enters the galactic field with these characteristics. The different contributions of single stars and binaries from individual star clusters which are selected from a power-law embedded star cluster mass function are then added up. This gives rise to integrated galactic field binary distribution functions (IGBDFs) resembling a galactic field's stellar content (Dynamical Population Synthesis). It is found that the binary proportion in the galactic field of a galaxy is larger the lower the minimum cluster mass, the lower the star formation rate, the steeper the embedded star cluster mass function and the larger the typical size of forming star clusters in the considered galaxy. In particular, period-, mass-ratio- and eccentricity IGBDFs for the Milky Way are modelled. The afore mentioned theoretical IGBDFs agree with independently observed distributions. Of all late-type binaries, 50% stem from M<300M_sun clusters, while 50% of all single stars were born in M>10^4M_sun clusters. Comparison of the G-dwarf and M-dwarf binary population indicates that the stars formed in mass-segregated clusters. In particular it is pointed out that although in the present model all M-dwarfs are born in binary systems, in the Milky Way's Galactic field the majority ends up being single stars. This work predicts that today's binary frequency in elliptical galaxies is lower than in spiral and in dwarf-galaxies. The period and mass-ratio distributions in these galaxies are explicitly predicted.

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