In this work, we analyse the dark matter (DM) fraction, fDM, and mass-to-light ratio mismatch parameter, dIMF (computed with respect to a Milky Way-like initialmass function), for a sample of 39 dwarf early-type galaxies in the Virgo cluster. Both fDM and δIMF are estimated within the central (one effective radius) galaxy regions, with a Jeans dynamical analysis that relies on galaxy velocity dispersions, structural parameters, and stellar mass-to-light ratios from the SMAKCED survey. In this first attempt to constrain, simultaneously, the initial mass function (IMF) normalization and the DM content, we explore the impact of different assumptions on the DM model profile. On average, for an Navarro, Frenk & White (NFW) profile, the dIMF is consistent with a Chabrier-like normalization (δIMF ~ 1), with fDM ~ 0.35. One of the main results of thiswork is that for at least a fewsystems the dIMF are heavier than the Milky Way-like value (i.e. either top- or bottom-heavy). When introducing tangential anisotropy, larger dIMF and smaller fDM are derived. Adopting a steeper concentration-mass relation than that from simulations, we find lower δIMF (≲ 1) and larger fDM. A constant M/L profile with null fDM gives the heaviest δIMF (~2). In the MONDian framework, we find consistent results to those for our reference NFW model. If confirmed, the large scatter of δIMF for dEs would provide (further) evidence for a non-universal IMF in early-type systems. On average, our reference fDM estimates are consistent with those found for low-σe (~ 100 kms-1) early-type galaxies (ETGs). Furthermore, we find fDM consistent with values from the SMAKCED survey, and find a double-value behaviour of fDM with stellar mass, which mirrors the trend of dynamical M/L and global star formation efficiency (from abundance matching estimates) with mass.
CITATION STYLE
Tortora, C., La Barbera, F., & Napolitano, N. R. (2016). Dark matter and IMF normalization in Virgo dwarf early-type galaxies. Monthly Notices of the Royal Astronomical Society, 455(1), 308–317. https://doi.org/10.1093/mnras/stv2250
Mendeley helps you to discover research relevant for your work.