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

By A. Zentner, Andrew P. Hearin, F. V. D. Bosch. 2013

174 citations recorded by the source corpus.

It is common practice for methods that use galaxy clustering to constrain the galaxy-halo relationship, such as the halo occupation distribution (HOD) and/or conditional luminosity function (CLF), to assume that halo mass alone suffices to determine a halo's resident galaxy population. Yet, the clustering strength of cold dark matter halos depends upon halo properties in addition to mass, such as formation time, an effect referred to as assembly bias. If galaxy characteristics are correlated with any of these auxiliary halo properties, the basic assumption of HOD/CLF methods is violated. We estimate the potential for assembly bias to induce systematic errors in inferred halo occupation statistics. We use halo abundance matching and age matching to construct fiducial mock galaxy catalogs that exhibit assembly bias as well as additional mock catalogs with identical HODs, but with assembly bias removed. We fit a parameterized HOD to the projected two-point clustering of mock galaxies in each catalog to assess the systematic errors induced by reasonable levels of assembly bias. In the absence of assembly bias, the inferred HODs generally describe the true underlying HODs well, validating the basic methodology. However, in all of the cases with assembly bias, the inferred HODs have systematic errors that are statistically significant. In most cases, these systematic errors cannot be identified using void statistics as auxiliary observables. We conclude that the galaxy-halo relationship inferred from galaxy clustering should be subject to a non-negligible systematic error induced by assembly bias. Our work suggests that efforts to model and/or constrain assembly bias should be high priorities as it is a threatening source of systematic error in galaxy evolution studies as well as the precision cosmology program.

View Paper: https://www.semanticscholar.org/paper/f90a2c8acf8575c54324c157301ebb11080b9f48

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Citations in the corpus

Disruption of Dark Matter Substructure: Fact or Fiction? (2017, methodology): “This has become a prime tool for interpreting galaxy clustering, galaxygalaxy lensing, and group multiplicity functions, and is even used to constrain cosmological parameters (Trujillo-Gomez et al. 2011; Hearin et al. 2015, 2016; Reddick et al. 2014; Zentner et al. 2014, 2016; Lehmann et al. 2015).”
Mapping stellar content to dark matter haloes using galaxy clustering and galaxy–galaxy lensing in the SDSS DR7 (2015, background): “However, it is worth noting that halo assembly bias may have a much greater impact on the HOD modelling of colour–selected galaxy samples, and could dominate the error budget on all mass scales for both colours (Zentner et al. 2014).”
Dark Matter Substructure in Numerical Simulations: A Tale of Discreteness Noise, Runaway Instabilities, and Artificial Disruption (2018, background): “…for interpreting galaxy clustering, galaxy-galaxy lensing, and group multiplicity functions, and is even used to constrain cosmological parameters (Maŕın et al. 2008; Trujillo-Gomez et al. 2011; Hearin et al. 2015, 2016; Reddick et al. 2013, 2014; Zentner et al. 2014, 2016; Lehmann et al. 2015).”
Lensing is Low: Cosmology, Galaxy Formation, or New Physics? (2016, methodology): “20 Standard HOD models have no assembly bias, whereas SHAM models based on Vpeak do have some levels of assembly bias (Zentner et al. 2014).”