The global mass-to-light ratio of SLACS lenses

Abstract

Aims. The dark matter content of early-type galaxies (ETGs) is a hotly debated topic with contrasting results arguing in favour of or against the presence of significant dark mass within the effective radius and the change with luminosity and mass. To address this question, we investigate here the global mass-to-light ratio Υ(r) = M(r)/L(r) of a sample of 21 lenses observed within the Sloan Lens ACS (SLACS) survey. Methods. We follow the usual approach of the galaxy as a two component systems, but we use a phenomenological ansatz for Υ(r), proposed by some of us, that is able to smoothly interpolate between constant M/L models and a wide class of dark matter haloes. The resulting galaxy model is then fitted to the data on the Einstein radius and aperture velocity dispersion. Results. Our phenomenological model turns out to agree with the data suggesting the presence of massive dark matter haloes to explain the lensing and dynamics properties of the SLACS lenses. According to the values of the dark matter mass fraction, we argue that the halo may play a significant role in the inner regions probed by the data, but such a conclusion strongly depends on the adopted initial mass function of the stellar population. Finally, we find that the dark matter mass fraction within R eff scales with both the total luminosity and stellar mass in such a way that more luminous (and hence more massive) galaxies have a greater dark matter content.

Dark matter scaling relations in intermediate z haloes

“…However, the slopes we find are strongly dependent on the model assumptions. Should we use the same NFW model, but the Chabrier IMF, we get f DM ( R eff ) ∝ L 0.20 V and f DM ( R eff ) ∝ M 0.16 ★ , which are fully consistent with the results we have obtained in Cardone et al (2009), where a general galaxy model has been fitted to a subsample of SLACS lenses 10…”

“…Note that, although (ϒ vir , log η X ) are the same for all the galaxies in a bin, ( M vir , r X ) still change from one lens to another thus allowing us to estimate all the quantities of interest on a lens‐by‐lens basis. In order to constrain (ϒ vir , log η X ), we maximize a suitable likelihood function, composed of two terms, the first (second) one referring to the lensing (dynamics) constraints (see Cardone et al 2009 for further details). In order to efficiently explore the parameter space, we use a Markov chain Monte Carlo (MCMC) algorithm running chains with 100 000 points reduced to more than 3000 after burn‐in cut and thinning.…”

“…10 10 Note that there is a typo in Cardone et al (2009), the best‐fitting relation being f DM ( R eff ) ∝ M 0.13 ★ , while the normalization refers to a Chabrier IMF. …”

Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

“…In fact, the total mass (dark plus baryonic) within the Einstein radius depends almost solely on the space-time geometry of the lensing system (the source and the lens redshift and the cosmological parameters). For this reason, strong lensing is a unique tool, if combined with central velocity dispersion measurements and stellar population analysis, to estimate the fraction of dark matter in the central regions of galaxy-scale halos (e.g., Gavazzi et al E-mail: petrillo@astro.rug.nl 2007; Jiang & Kochanek 2007;Grillo et al 2010;Cardone et al 2009;Cardone & Tortora 2010;Tortora et al 2010;More et al 2011;Ruff et al 2011;Sonnenfeld et al 2015), and to constrain the slope of the inner mass density profile (e.g., Treu & Koopmans 2002a,b;Koopmans et al 2006;Koopmans & Treu 2003;More et al 2008;Barnabè et al 2009;Koopmans et al 2009;Cao et al 2016). …”

The last 6 Gyr of dark matter assembly in massive galaxies from the Kilo Degree Survey

“…The claimed correlation with mass seems almost insensitive to the adopted galaxy mass profile or initial mass function, IMF (e.g., Cardone et al 2009;Cardone & Tortora 2010;Cardone et al 2011), but it can become uncertain in case a non-ΛCDM scenario, with mass following the (non-homologous) light distribution, is adopted (e.g., Trujillo et al 2004;Tortora et al 2009Tortora et al , 2012. The scenario is even more complicated if one takes into account the effect of a non universal IMF (van Dokkum & Conroy 2010;Treu et al 2010;Thomas et al 2011;Conroy & van Dokkum 2012;Cappellari et al 2012Cappellari et al , 2013Spiniello et al 2012;Wegner et al 2012;Barnabè et al 2013;Dutton et al 2013;Ferreras et al 2013;Goudfrooij & Kruijssen 2013;La Barbera et al 2013;Tortora et al 2013;Weidner et al 2013;Goudfrooij & Kruijssen 2014;Shu et al 2015;McDermid et al 2014;Tortora et al 2014a,c;Martín-Navarro et al 2015;Spiniello et al 2015;Lyubenova et al 2016;Tortora et al 2016;Corsini et al 2017;Li et al 2017;Sonnenfeld et al 2017;Tortora et al 2017).…”