Constraining Cosmology using Galaxy Clusters¶
Reviews¶
Allen2011
- Cosmological Parameters from Observations of Galaxy Clusters - ARA&A review by Allen, Evrard & Mantz 2011
- Huterer et al. 2015 - Growth of cosmic structure: Probing dark
energy beyond
expansion
- See Section 4.2: Cluster Abundance
- The primary importance of clusters in the context of dark energy is their complementarity to geometric probes, i.e. their ability to distinguish between modified gravity and dark energy models with degenerate expansion histories. Galaxy clusters are statistically competitive with and often better than other probes. (Fig 5, right panel, the “sweet spot” is z=0.3 - 0.8.
- The basic physics behind cluster abundances as a cosmological probe are conceptually simple; and A cluster abundance experiment is conceptually very simple.
- The dependence of the number of galaxy clusters on the variance of the linear density field that allows us to utilize galaxy clusters to constrain the growth of structure.
- Future surveys will almost certainly rely on weak lensing mass calibration to estimate cluster masses.
- Other usages of clusters as a cosmological tool:
- Galaxy clusters can probe dark energy in other ways as well, most notably by comparing cluster mass estimates from weak lensing and dynamical methods such as galaxy velocity dispersions
- Because the growth of structure is also impacted by non-zero neutrino mass, galaxy cluster abundances can provide competitive constraints on the sum of neutrino masses.
- Difficulties and limitations:
- To achieve the goal of DES Stage III (IV) requirement, we must be able to measure cluster masses with 5% (2%) precision. See Applegate et al. (2014) (Currently can achieve 7%, but with 20% systematic differences among methods).
- Systematic errors in shear measurements tend to be less critical for cluster abundance work than for cosmic shear work (only care about circular averaged tangential shear).
- Key systematic is the calibration not only of the mean relation between cluster observables (optical, X-ray, or mm signals) and cluster mass, but also the scatter (shape and amplitude) about the mean.
- Cluster centering remains an important systematic in optical and/or low resolution experiments (e.g. Planck). (See Oguri & Takada 2011 about self-calibration).
- Optical observations benefit from a lower mass detection threshold than X-ray/mm over a large redshift range, which in turn result in improved statistical constraints.
- The synergistic nature of multi-wavelength cluster cosmology will necessarily play a key role in future cluster abundance experiments. A balanced multi-wavelength approach will be critical to the success of cluster cosmology over the next 10–20 years.
- Self-calibration: the cluster-clustering signal is itself an observable that one can use to calibrate cluster masses, and which is insensitive to all of the above systematic effects.
- Huterer & Shafer 2018 - Dark energy two decades after: observables,
probes, consistency
tests
- See Section 5.5 Galaxy Clusters
- Clusters are versatile probes of cosmology and astrophysics and have had an important role in the development of modern cosmology
- Recent cluster observations typically do not have enough signal-to-noise to determine the cluster masses directly; instead, forward-modeling can be applied to the mass function to recast the theory in the space of observable quantities (e.g. see Evrard et al. 2014).
- The mass function’s near-exponential dependence on the power spectrum in the high-mass limit is at the root of the power of clusters to probe the growth of density fluctuations.
- (The clusters’) two-point correlation function probes the matter power spectrum as well as the growth and geometry factors sensitive to dark energy.
- Clusters can also be correlated with background galaxies to probe the growth (Cluster-Galaxy lensing, see Oguri & Takada 2011
- The most important uncertainty is typically tied to parameters that describe the scaling relations between mass and observable properties of the cluster (e.g. Flux, temperature).
- Weinberg 2013, PhR - Observational probes of cosmic
acceleration
- See Section 6: Clusters of galaxies
Key Papers¶
Theoretical¶
Predictions¶
- Pillepich et al. 2018 - Forecasts on dark energy from the X-ray
cluster survey with eROSITA: constraints from counts and
clustering
- Fisher information is extracted from the number density and spatial clustering of a photon-count-limited sample of clusters of galaxies up to z ˜ 2. We consider different scenarios for the availability of (i) X-ray follow-up observations, (ii) photometric and spectroscopic redshifts, and (iii) accurate knowledge of the observable - mass relation down to the scale of galaxy groups, but no additional observation-related systematics are taken into account.
Early stage¶
- Wang & Steinhardt 1998 - Cluster Abundance Constraints for Cosmological Models with a Time-varying, Spatially Inhomogeneous Energy Component with Negative Pressure
- Haiman, Mohr & Holder 2001, ApJ - Constraints on Cosmological
Parameters from Future Galaxy Cluster
Surveys
- Important: “Our results indicate a formal statistical uncertainty of ~3% (68% confidence) on both Ωm and w when the SZE survey is combined with either the CMB or SN data; a large number of clusters in the X-ray survey further suppresses the degeneracy between w and both Ωm and h.”
- Fan & Chiueh 2001 - Determining the Geometry and the Cosmological Parameters of the Universe through Sunyaev-Zeldovich Effect Cluster Counts
- Holder, Haiman & Mohr 2001 - Constraints on Ωm, ΩΛ, and σ8 from Galaxy Cluster Redshift Distributions
- Newman et al. 2002 - Measuring the Cosmic Equation of State with Galaxy Clusters in the DEEP2 Redshift Survey
- Refregier et al. 2002 - Cosmology with galaxy clusters in the XMM large-scale structure survey
- Levine et al. 2002 - Future Galaxy Cluster Surveys: The Effect of Theory Uncertainty on Constraining Cosmological Parameters
Systematic, (Self-)Calibration¶
- Lima & Hu 2004 - Self-calibration of cluster dark energy studies:
Counts in
cells
- Important Self-calibration (Using noise as signal!): The excess variance of counts due to the clustering of clusters provides such an opportunity and can be measured from the survey without additional observational cost.
- Lima & Hu 2005 - Self-calibration of cluster dark energy studies:
Observable-mass
distribution
- Given the shape of the actual mass function, the properties of the distribution may be internally monitored by the shape of the observable mass function.
- Hu & Cohn 2006 - Likelihood methods for cluster dark energy surveys
- Lima & Hu 2007 - Photometric redshift requirements for
self-calibration of cluster dark energy
studies
- Self-calibration in combination with external mass inferences helps reduce photo-z requirements and provides important consistency checks for future cluster surveys.
- Majumdar & Mohr 2003 - Importance of Cluster Structural Evolution in
Using X-Ray and Sunyaev-Zeldovich Effect Galaxy Cluster Surveys to
Study Dark
Energy
- We show that for a particular X-ray survey (Sunyaev-Zeldovich effect [SZE] survey), the constraints on w degrade by roughly a factor of 3 (factor of 2) when one accounts for the possibility of nonstandard cluster evolution.
- Majumdar & Mohr 2004 - Self-Calibration in Cluster Studies of Dark
Energy: Combining the Cluster Redshift Distribution, the Power
Spectrum, and Mass
Measurements
- The best constraints are obtained when one combines both the power spectrum constraints and the mass measurements with the cluster redshift distribution; when using the survey to extract the parameters and evolution of the mass-observable relations, we estimate uncertainties on w of ~4%-6%
- Hu 2003 - Self-consistency and calibration of cluster number count
surveys for dark
energy
- “we find that the ambiguity from the normalization of the mass-observable relationships, or an extrapolation of external mass-observable determinations from higher masses, can be largely eliminated with a sufficiently deep survey, even allowing for an arbitrary evolution”
- Wu, Rozo & Wechsler 2008 - The Effects of Halo Assembly Bias on
Self-Calibration in Galaxy Cluster
Surveys
- Halo assembly bias: the clustering amplitude of halos depends not only on the halo mass, but also on various secondary variables.
- The impact of the secondary dependence is determined by (1) the scatter in the observable-mass relation and (2) the correlation between observable and secondary variables. Could be important to DES and LSST like survey
- Cunha 2009 - Cross-calibration of cluster mass
observables
- We use a Fisher matrix analysis to study the improvements in the joint dark energy and cluster mass-observables constraints resulting from combining cluster counts and clustering abundances measured with different techniques.
- The cross-calibrated constraints are less sensitive to variations in the mass threshold or maximum redshift range.
- Cunha, Huterer & Frieman 2009 - Constraining dark energy with
clusters: Complementarity with other
probes
- We find that optimally combined optical and Sunyaev-Zeldovich effect cluster surveys should improve the Dark Energy Task Force figure of merit
- Wu, Rozo & Wechsler 2010 - Annealing a Follow-up Program:
Improvement of the Dark Energy Figure of Merit for Optical Galaxy
Cluster
Surveys
- Considering clusters selected from optical imaging in the Dark Energy Survey, we find that approximately 200 low-redshift X-ray clusters or massive Sunyaev-Zel’dovich clusters can improve the dark energy figure of merit by 50%, provided that the follow-up mass measurements involve no systematic error.
- The scatter in the optical richness–mass distribution, which needs to be made as tight as possible to improve the efficacy of follow-up observations
- Oguri & Takada 2011, PhRvD - Combining cluster observables and stacked weak lensing to probe dark energy: Self-calibration of systematic uncertainties
- Rozo et al. 2011, ApJ - Stacked Weak Lensing Mass Calibration: Estimators, Systematics, and Impact on Cosmological Parameter Constraints
- Evrard, Arnault, Huterer & Farahi 2014 - A model for multiproperty
galaxy cluster
statistics
- We derive closed-form expressions for the space density of haloes as a function of multiple observables as well as forms for the low-order moments of properties of observable-selected samples.
Other issues¶
- Takada & Bridle 2007, NJPh - Probing dark energy with cluster counts and cosmic shear power spectra: including the full covariance
- Ichiki & Takada 2012, PhRvD - Impact of massive neutrinos on the abundance of massive clusters
- Takada & Spergel 2014, MNRAS - Joint analysis of cluster number counts and weak lensing power spectrum to correct for the super-sample covariance
Observational¶
- Chandra Cluster Cosmology Project
- Vilhlinin et al. 2009a - Chandra Cluster Cosmology Project. II. Samples and X-Ray Data Reduction
- Vilhlinin et al. 2009b - Chandra Cluster Cosmology Project III:
Cosmological Parameter
Constraints
- 37 Chandra clusters at = 0.55 from ROSAT and 49 brightest z=0.05 clusters
- The observed growth of massive galaxy clusters using
ROSAT/Chandra
- Mantz, Allen, Rapetti & Ebeling 2010a - I. Statistical methods
and cosmological
constraints
- 238 clusters from RASS; 94 Chandra follow-up.
- Mantz, Allen, Ebeling, Rapetti & Drlica-Wagner 2010 - II. X-ray scaling relations
- Rapetti, Allen, Mantz & Ebeling 2010 - III. Testing general relativity on cosmological scales
- Mantz, Allen & Rapetti 2010 - IV. Robust constraints on neutrino properties
- Mantz, Allen, Rapetti & Ebeling 2010a - I. Statistical methods
and cosmological
constraints
- maxBCG clusters
- Rozo et al. 2010 - Cosmological Constraints from the Sloan
Digital Sky Survey maxBCG Cluster
Catalog
- SDSS-maxBCG: fully consistent with the WMAP five-year data, and in a joint analysis we find σ8 = 0.807 ± 0.020 and Ωm = 0.265 ± 0.016
- Zu et al. 2014, MNRAS - Cosmological constraints from the large-scale weak lensing of SDSS MaxBCG clusters
- Rozo et al. 2010 - Cosmological Constraints from the Sloan
Digital Sky Survey maxBCG Cluster
Catalog
- Tinker et al. 2012 - Cosmological Constraints from Galaxy Clustering
and the Mass-to-number Ratio of Galaxy
Clusters
- SDSS 2PCF + mass-to-galaxy number ratio within cluster
- Cosmology and astrophysics from relaxed galaxy clusters in Chandra
& ROSAT
- Mantz et al. 2015 - I. Sample selection
- Mantz et al. 2014 - II. Cosmological constraints
- Mantz et al. 2016 - III. Thermodynamic profiles and scaling relations
- Applegate et al. 2016 - IV. Robustly calibrating hydrostatic masses with weak lensing
- Mantz et al. 2016 - V. Consistency with cold dark matter structure formation
- de Haan et al. 2016 - Cosmological Constraints from Galaxy Clusters
in the 2500 Square-degree SPT-SZ
Survey
- 377 clusters at z>0.2 from 2500 square-degree South Pole Telescope SZ survey
Cluster mass calibration and scaling relations¶
- Becker & Kravtsov 2011 - On the Accuracy of Weak-lensing Cluster
Mass
Reconstructions
- Important: We find that correlated large-scale structure within several virial radii of clusters contributes a smaller, but nevertheless significant, amount to the scatter. The intrinsic scatter due to these physical sources is ≈20% for massive clusters and can be as high as ≈30% for group-sized systems.
- We find that WL mass measurements can have a small, ≈5%-10%, but non-negligible amount of bias.
- Rasia et al. 2012 - Lensing and x-ray mass estimates of clusters
(simulations)
- We confirm previous results showing that lensing masses obtained from the fit of the cluster tangential shear profiles with Navarro-Frenk-White functionals are biased low by ˜5-10% with a large scatter (˜10-25%)
- Rozo, Bartlett, Evrard & Rykoff 2014 - Closing the loop: a
self-consistent model of optical, X-ray and Sunyaev-Zel’dovich
scaling relations for clusters of
Galaxies
- We find that scaling relations derived from optical and X-ray selected cluster samples are consistent with one another. These cluster scaling relations satisfy several non- trivial spatial abundance constraints and closure relations.
Using Velocity Distribution Function¶
- The Velocity Distribution Function of Galaxy Clusters as a Cosmological Probe
- Cluster Cosmology with the Velocity Distribution Function of the HeCS-SZ Sample
- On the Cluster Physics of Sunyaev-Zeldovich and X-Ray Surveys
- Battaglia, Bond, Pfommer & Sievers 2012a - I. The Influence of Feedback, Non-thermal Pressure, and Cluster Shapes on Y-M Scaling Relations
- Battaglia, Bond, Pfommer & Sievers 2012b - II. Deconstructing the Thermal SZ Power Spectrum
- Battaglia, Bond, Pfommer & Sievers 2013 - III. Measurement Biases and Cosmological Evolution of Gas and Stellar Mass Fractions
- Penna-Lima et al. 2017 - Calibrating the Planck cluster mass scale
with CLASH
- 1 - b_sz = 0.73 +/- 0.10
Lectures and Conferences¶
- Ushering in DES Cluster Cosmology with redMaPPer by Eduardo
Rozo
- Galaxy clusters are the most massive gravitationally bound structures in the Universe.
- Number of galaxy clusters as a function of halo mass measures the amount of structure in the Universe (sigma_8).
- Optical selection allows detection of low mass systems; more abundant == better weak lensing halo mass == Better cosmology. Finding clusters in the optical maximizes the cosmological information that can be drawn from clusters.
- Centering cluster is hard!
- SLAC-2017 Conference on Cluster Cosmology
- Cosmology with Clusters of Galaxies by Ben Maughan (Undergraduate Level)
- KITP Conference: Astrophysics and Cosmology with Galaxy Clusters
Important References¶
- Galaxy clusters have been recognized as powerful cosmological probes
- Henry et al. 2009; Vikhlinin et al. 2009; Mantz et al. 2010; Rozo et al. 2010; Clerc et al. 2012; Benson et al. 2013; Hasselfield et al. 2013).
- Early optical cluster finders can be divided into roughly two classes
- Those based on photometric redshifts
- Kepner et al. 1999; van Breukelen & Clewley 2009; Milkeraitis et al. 2010; Durret et al. 2011; Szabo et al. 2011; Soares-Santos et al. 2011; Wen et al. 2012
- Those utilizing the cluster red sequence
- Annis et al. 1999; Gladders & Yee 2000; Koester et al. 2007a; Gladders et al. 2007; Gal et al. 2009; Thanjavur et al. 2009; Hao et al. 2010; Murphy et al. 2012
- Those based on photometric redshifts