Constraining Satellite Galaxy Stellar Mass Loss and Predicting Intrahalo Light. I. Framework and Results at Low Redshift

Abstract

We introduce a new technique that uses galaxy clustering to constrain how satellite galaxies lose stellar mass and contribute to the diffuse "intrahalo light" (IHL). We implement two models that relate satellite galaxy stellar mass loss to the detailed knowledge of subhalo dark matter mass loss. Model 1 assumes that the fractional stellar mass loss of a galaxy, from the time of merging into a larger halo until the final redshift, is proportional to the fractional amount of dark matter mass loss of the subhalo it lives in. Model 2 accounts for a delay in the time that stellar mass is lost due to the fact that the galaxy resides deep in the potential well of the subhalo and the subhalo may experience dark matter mass loss for some time before the galaxy is affected. We use these models to predict the stellar masses of a population of galaxies and we use abundance matching to predict the clustering of several r-band luminosity threshold samples from the Sloan Digital Sky Survey. Abundance matching assuming no stellar mass loss (akin to abundance matching at the time of subhalo infall) overestimates the correlation function on small scales ( 1 Mpc), while allowing too much stellar mass loss leads to an underestimate of small-scale clustering. For each luminosity threshold sample, we are thus able to constrain the amount of stellar mass loss required to match the observed clustering. We find that satellite galaxy stellar mass loss is strongly luminosity dependent, with less luminous satellite galaxies experiencing substantially more efficient stellar mass loss than luminous satellites. With constrained stellar mass loss models, we can infer the amount of stellar mass that is deposited into the IHL. We find that both of our model predictions for the mean amount of IHL as a function of halo mass are consistent with current observational measurements. However, our two models predict a different amount of scatter in the IHL from halo to halo, with Model 2 being favored by observations. This demonstrates that a comparison to IHL measurements provides independent verification of our stellar mass loss models, as well as additional constraining power.

Galaxy assembly bias: a significant source of systematic error in the galaxy–halo relationship

“…By itself this is an interesting point because abundance matching and age matching yield galaxy populations with clustering statistics that are broadly similar to those observed in large-scale galaxy surveys (e.g. Kravtsov et al 2004;Vale & Ostriker 2004;Tasitsiomi et al 2004;Vale & Ostriker 2006;Conroy & Wechsler 2009;Guo et al 2010;Simha et al 2010;Neistein et al 2011;Watson et al 2012;Rodríguez-Puebla et al 2012;Kravtsov 2013;. This suggests that the assembly bias predicted by abundance matching may not be wildly different from what is observationally permissible.…”

Intracluster light properties in the CLASH-VLT cluster MACS J1206.2-0847

“…The absence or mildness of these trends is also confirmed at lower redshifts, i.e., z < 0.3, (Zibetti et al 2005;Krick & Bernstein 2007). These findings are inconsistent with most of the previous results from both cosmological and analytical simulations, which generally agree with an increasing ICL fraction as cluster mass grows (Murante et al 2004;Lin & Mohr 2004;Purcell et al 2007;Watson et al 2012). However, recent simulations suggest a much weaker dependence of the ICL fraction on cluster mass (Murante et al 2007;Dolag et al 2010;Puchwein et al 2010;Martel et al 2012;Cui et al 2014).…”

Subhalo abundance matching and assembly bias in the EAGLE simulation

“…Conroy et al 2006;Behroozi et al 2010;Guo et al 2010;Moster et al 2010;Wetzel & White 2010;TrujilloGomez et al 2011;Watson, Berlind & Zentner 2012;Nuza et al 2013;Reddick et al 2013). For instance, Conroy et al (2006) showed that SHAM reproduces the observed galaxy clustering over a broad redshift interval (0 < z < 5).…”