Microwave Sky Simulations and Projections for Galaxy Cluster Detection with the Atacama Cosmology Telescope

Sehgal, Neelima; Trac, Hy; Huffenberger, K. M.; Bode, Paul

doi:10.1086/518880

Cited by 38 publications

(66 citation statements)

References 64 publications

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“…This is expected because of the higher noise level of SPT compared to ACT. Our spurious detection rate is similar to what found by Melin et al (2006) and Sehgal et al (2007). Only the latter has a slightly lower completeness ≈90% for masses M ≥ 3 × 10 14 M /h because they accounted the contamination of point sources and dust emission, and because they used a multi-frequency Wiener filter.…”

Section: Statistics Of Sz Multi-band Detectionssupporting

confidence: 84%

Statistical properties of SZ and X-ray cluster detections

Pace

Maturi

Bartelmann

et al. 2008

A&A

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Aims. We calibrate the number density, completeness, reliability, and the lower mass limit of galaxy-cluster detections through their thermal SZ signal and compare them to X-ray cluster detections. Methods. We simulate maps of the thermal SZ effect and the X-ray emission from light cones constructed in a large, hydrodynamical, cosmological simulation volume, including realistic noise contributions. The maps are convolved with linear, optimised, single-and multi-band filters to identify local peaks and their signal-to-noise ratios. The resulting peak catalogues are then compared to the halo population in the simulation volume to identify true and spurious detections. Results. Multi-band filtering improves the statistics of SZ cluster detections considerably compared to single-band filtering. Observations with the characteristics of ACT detect clusters with masses M ≥ 6−9 × 10 13 M /h, quite independent of redshift, reach 50% completeness at ∼1014 M /h and 100% completeness at ∼2× 10 14 M /h. Samples are contaminated by a few spurious detections, but they are only a small percentage of all detections. This is broadly comparable to X-ray cluster detections with XMM-Newton with 100 ks exposure time in the soft band, except that the mass limit for X-ray detections increases much more steeply with redshift than for SZ detections. A comparison of true and filtered signals in the SZ and X-ray maps confirms that the filters introduce at most a negligible bias.

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Section: Statistics Of Sz Multi-band Detectionssupporting

confidence: 84%

Statistical properties of SZ and X-ray cluster detections

Pace

Maturi

Bartelmann

et al. 2008

A&A

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“…Further complications arise because the measured signal is not due only to the Sunyaev-Zeldovich distortions, but also contains the blackbody primordial microwave fluctuations, gravitational lensing of the microwave background, infrared and radio point sources which can be correlated with galaxy cluster positions, and galactic dust (see [60] for a description of sky simulations incorporating all of these signals). The kinematic SZ signal must be separated from all of the others via a combination of frequency and spatial filtering.…”

Section: Discussionmentioning

confidence: 99%

Dark energy constraints from galaxy cluster peculiar velocities

2008

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Future multifrequency microwave background experiments with arcminute resolution and microKelvin temperature sensitivity will be able to detect the kinetic Sunyaev-Zeldovich effect, providing a way to measure radial peculiar velocities of massive galaxy clusters. We show that cluster peculiar velocities have the potential to constrain several dark energy parameters. We compare three velocity statistics (the distribution of radial velocities, the mean pairwise streaming velocity, and the velocity correlation function) and analyze the relative merits of these statistics in constraining dark energy parameters. Of the three statistics, mean pairwise streaming velocity provides constraints that are least sensitive to velocity errors: the constraints on parameters degrades only by a factor of two when the random error is increased from 100 to 500 km/s. We also compare cluster velocities with other dark energy probes proposed in the Dark Energy Task Force report. For cluster velocity measurements with realistic priors, the eventual constraints on the dark energy density, the dark energy equation of state and its evolution are comparable to constraints from supernovae measurements, and better than cluster counts and baryon acoustic oscillations; adding velocity to other dark energy probes improves constraints on the figure of merit by more than a factor of two. For upcoming Sunyaev-Zeldovich galaxy cluster surveys, even velocity measurements with errors as large as 1000 km/s will substantially improve the cosmological constraints compared to using the cluster number density alone.PACS numbers: 95.36.+x, 98.65.Cw,

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“…The simulated volume is a periodic cube of size L = 1500 h −1 Mpc, the particle mass m p = 1.22 × 10 11 h −1 M , and the cubic spline softening length = 16.5 h −1 kpc. This simulation was carried out with exactly the same methods used for the simulations described by Bode et al (2007), Sehgal et al (2007), and Shaw et al (2008); so, for a description of the computational details, we refer the reader to these papers.…”

Section: Simulationsmentioning

confidence: 99%

Sharpening the Precision of the Sunyaev-Zel'dovich Power Spectrum

Shaw

Zahn

Holder

et al. 2009

ApJ

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Using both halo model calculations and a large sample of simulated SZ maps, we demonstrate that high-mass clusters add significant non-Gaussian variance to measurements of the SZ power spectrum amplitude. The difficulty in correctly accounting theoretically for the contribution of these objects to the uncertainty in C leads to a reduced sensitivity to σ 8 . We show that a simple solution is to mask out the brightest clusters in the map before measuring the power spectrum. We demonstrate that fairly conservative masking can reduce the variance and Gaussianize the statistics significantly, thus increasing the sensitivity to cosmological parameters. Choosing which objects to mask is nontrivial; we found that using a fixed sky density produced a well-defined and well-behaved estimate that can easily be applied to real maps. For example, masking the 10 (90) brightest clusters in a 100 deg 2 SZ map will improve the sensitivity to C by a factor of two at = 1000 (2000) and 1.5 at = 2000 (4000). We show that even in the presence of astrophysical foregrounds (primary cosmic microwave background and point sources) and instrument noise, one can increase the precision on measurements of σ 8 by masking up to 0.9 clusters deg −2 .

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Microwave Sky Simulations and Projections for Galaxy Cluster Detection with the Atacama Cosmology Telescope

Cited by 38 publications

References 64 publications

Statistical properties of SZ and X-ray cluster detections

Statistical properties of SZ and X-ray cluster detections

Dark energy constraints from galaxy cluster peculiar velocities

Sharpening the Precision of the Sunyaev-Zel'dovich Power Spectrum

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