4.7 Article

Detailed chemical evolution models of the Tucana dSph galaxy

Journal

MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
Volume 501, Issue 2, Pages 2562-2571

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/mnras/staa3507

Keywords

stars: formation; ISM: evolution; galaxies: abundances; galaxies: dwarf; galaxies: evolution; galaxies: Local Group

Funding

  1. Universidad Iberoamericana
  2. PAPIIT (DGAPA-UNAM) [IA100420]

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The study reveals that the dwarf spheroidal galaxy Tucana behaved like a closed box for majority of its lifespan, with different metallicity models at different stages. Observing the metallicity distribution function and improving observation precision are crucial to differentiate between the models.
We use semi-analytical methods to obtain detailed chemical evolution models (CEMs) for the dwarf spheroidal (dSph) galaxy Tucana. Published star formation rates and the age-metallicity relationship are used to constrain the observables. The results show that Tucana: (i) behaved like a closed box for 75 per cent of its life, (ii) had either a primordial-gas accretion or a metal-rich wind during 15 per cent of its life (between 0.5 and 2.0 Gyr), and (iii) lost 95 per cent of its gas through a well-mixed wind at t similar to 4.5 Gyr. Specifically, we find two CEMs: the metal-dilution model and the metal-loss model, which differ mainly during the range 0.5-2.0 Gyr. In order to discriminate between these CEMs, we compare the predicted [Xi/Fe]-[Fe/H] trends, which differ less than the average error of the observed trends for other dSphs of the Local Group. Furthermore, the models predict very different metallicity distribution functions. Therefore, an observational metallicity distribution function for Tucana is essential in order to discriminate between the metal-dilution and the metal-loss scenarios. In addition, because the difference of [Xi/Fe] between the two models is less than the average of the errors observed for others dSph and ultra-faint dwarf galaxies of the Local Group, greater precision is required in future observations, so that the errors are less than the difference between the models, thus enabling discrimination between them.

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