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The Dependence of Halo Clustering on Halo Formation History, Concentration, and Occupation
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We investigate the dependence of dark matter halo clustering on halo formation time, density profile concentration, and subhalo occupation number, using high-resolution numerical simulations of a ΛCDM cosmology. We confirm results that halo clustering is a function of halo formation time at fixed mass and that this trend depends on halo mass. For the first time, we show unequivocally that halo clustering is a function of halo concentration and show that the dependence of halo bias on concentration, mass, and redshift can be accurately parameterized in a simple way: b ( M , c | z ) = b ( M | z ) b ( c | M / M ⋆ ). Interestingly, the scaling between bias and concentration changes sign with the value of M / M ⋆ : high-concentration (early forming) objects cluster more strongly for M ≲ M ⋆ , while low-concentration (late forming) objects cluster more strongly for rare high-mass halos, M ≳ M ⋆ . We show the first explicit demonstration that host dark halo clustering depends on the halo occupation number (of dark matter subhalos) at fixed mass and discuss implications for halo model calculations of dark matter power spectra and galaxy clustering statistics. The effect of these halo properties on clustering is strongest for early-forming dwarf-mass halos, which are significantly more clustered than typical halos of their mass. Our results suggest that isolated low-mass galaxies (e.g., low surface brightness dwarfs) should have more slowly rising rotation curves than their more clustered counterparts and may have consequences for the dearth of dwarf galaxies in voids. They also imply that self-calibrating richness-selected cluster samples with their clustering properties might overestimate cluster masses and bias cosmological parameter estimation.
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Boylan-Kolchin, M., Springel, V., White, S. D. M., Jenkins, A., & Lemson, G. (2009) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2009.15191.x · Cited by 909
Desjacques, V., Jeong, D., & Schmidt, F. (2018) · Physics Reports · doi:10.1016/j.physrep.2017.12.002 · Cited by 817
The statistics of CDM halo concentrations
Neto, A. F., Gao, L., Bett, P., Cole, S., Navarro, J. F., Frenk, C. S., White, S. D. M., Springel, V., & Jenkins, A. (2007) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2007.12381.x · Cited by 764
Maccio, A. V., Dutton, A. A., Van Den Bosch, F. C., Moore, B., Potter, D., & Stadel, J. (2007) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2007.11720.x · Cited by 553
Metadata
- Authors
- Wechsler, R. H., Zentner, A. R., Bullock, J. S., Kravtsov, A. V., & Allgood, B.
- Venue
- The Astrophysical Journal
- Publisher
- American Astronomical Society
- Published
- 2006-11-20
- Volume
- 652
- Issue
- 1
- Pages
- 71-84
- ISSN
- 0004-637X (ISSN-L)
- 1538-4357
- OpenAlex
- W2023351067
- Cited by
- 567
- References
- 101
- FWCI
- 22.56
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.