How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics
Abstract
ABSTRACT Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analysed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy–halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function wp(rp), the galaxy–galaxy lensing signal ΔΣ(rp), the void probability function VPF(r), the distributions of counts-in-cylinders P(NCIC), and counts-in-annuli P(NCIA), and the distribution of the ratio of counts in cylinders of different sizes P(N2/N5). We find that despite the frequent use of the combination wp(rp) + ΔΣ(rp) in interpreting galaxy data, the count statistics, P(NCIC) and P(NCIA), are generally more efficient in constraining galaxy assembly bias whenmore »
- Authors:
-
[1];
[1];
[1];
[2];
[2];
[6]
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA, Pittsburgh Particle Physics, Astrophysics, and Cosmology Center (PITT PACC), University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Astronomy, Yale University, PO Box 208101, New Haven, CT 06511, USA
- Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA, McWilliams Center for Cosmology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260, USA, Argonne National Laboratory, Argonne, IL 60539, USA
- Argonne National Laboratory, Argonne, IL 60539, USA
- McWilliams Center for Cosmology, Carnegie Mellon University, Pittsburgh, PA 15213, USA, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1545973
- Grant/Contract Number:
- AC02-06CH11357
- Resource Type:
- Published Article
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 488 Journal Issue: 3; Journal ID: ISSN 0035-8711
- Publisher:
- Oxford University Press
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Wang, Kuan, Mao, Yao-Yuan, Zentner, Andrew R., van den Bosch, Frank C., Lange, Johannes U., Schafer, Chad M., Villarreal, Antonio S., Hearin, Andrew P., and Campbell, Duncan. How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics. United Kingdom: N. p., 2019.
Web. https://doi.org/10.1093/mnras/stz1733.
Wang, Kuan, Mao, Yao-Yuan, Zentner, Andrew R., van den Bosch, Frank C., Lange, Johannes U., Schafer, Chad M., Villarreal, Antonio S., Hearin, Andrew P., & Campbell, Duncan. How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics. United Kingdom. https://doi.org/10.1093/mnras/stz1733
Wang, Kuan, Mao, Yao-Yuan, Zentner, Andrew R., van den Bosch, Frank C., Lange, Johannes U., Schafer, Chad M., Villarreal, Antonio S., Hearin, Andrew P., and Campbell, Duncan. Tue .
"How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics". United Kingdom. https://doi.org/10.1093/mnras/stz1733.
@article{osti_1545973,
title = {How to optimally constrain galaxy assembly bias: supplement projected correlation functions with count-in-cells statistics},
author = {Wang, Kuan and Mao, Yao-Yuan and Zentner, Andrew R. and van den Bosch, Frank C. and Lange, Johannes U. and Schafer, Chad M. and Villarreal, Antonio S. and Hearin, Andrew P. and Campbell, Duncan},
abstractNote = {ABSTRACT Most models for the statistical connection between galaxies and their haloes ignore the possibility that galaxy properties may be correlated with halo properties other than halo mass, a phenomenon known as galaxy assembly bias. And yet, it is known that such correlations can lead to systematic errors in the interpretation of survey data that are analysed using traditional halo occupation models. At present, the degree to which galaxy assembly bias may be present in the real Universe, and the best strategies for constraining it remain uncertain. We study the ability of several observables to constrain galaxy assembly bias from redshift survey data using the decorated halo occupation distribution (dHOD), an empirical model of the galaxy–halo connection that incorporates assembly bias. We cover an expansive set of observables, including the projected two-point correlation function wp(rp), the galaxy–galaxy lensing signal ΔΣ(rp), the void probability function VPF(r), the distributions of counts-in-cylinders P(NCIC), and counts-in-annuli P(NCIA), and the distribution of the ratio of counts in cylinders of different sizes P(N2/N5). We find that despite the frequent use of the combination wp(rp) + ΔΣ(rp) in interpreting galaxy data, the count statistics, P(NCIC) and P(NCIA), are generally more efficient in constraining galaxy assembly bias when combined with wp(rp). Constraints based upon wp(rp) and ΔΣ(rp) share common degeneracy directions in the parameter space, while combinations of wp(rp) with the count statistics are more complementary. Therefore, we strongly suggest that count statistics should be used to complement the canonical observables in future studies of the galaxy–halo connection.},
doi = {10.1093/mnras/stz1733},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 3,
volume = 488,
place = {United Kingdom},
year = {2019},
month = {6}
}
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