256 research outputs found
Reconstructing Probability Distributions with Gaussian Processes
Modern cosmological analyses constrain physical parameters using Markov Chain
Monte Carlo (MCMC) or similar sampling techniques. Oftentimes, these techniques
are computationally expensive to run and require up to thousands of CPU hours
to complete. Here we present a method for reconstructing the log-probability
distributions of completed experiments from an existing MCMC chain (or any set
of posterior samples). The reconstruction is performed using Gaussian process
regression for interpolating the log-probability. This allows for easy
resampling, importance sampling, marginalization, testing different samplers,
investigating chain convergence, and other operations. As an example use-case,
we reconstruct the posterior distribution of the most recent Planck 2018
analysis. We then resample the posterior, and generate a new MCMC chain with
forty times as many points in only thirty minutes. Our likelihood
reconstruction tool can be found online at
https://github.com/tmcclintock/AReconstructionTool.Comment: 7 pages, 4 figures, repository at
https://github.com/tmcclintock/AReconstructionToo
The Ysz--Yx Scaling Relation as Determined from Planck and Chandra
SZ clusters surveys like Planck, the South Pole Telescope, and the Atacama
Cosmology Telescope, will soon be publishing several hundred SZ-selected
systems. The key ingredient required to transport the mass calibration from
current X-ray selected cluster samples to these SZ systems is the Ysz--Yx
scaling relation. We constrain the amplitude, slope, and scatter of the Ysz--Yx
scaling relation using SZ data from Planck, and X-ray data from Chandra. We
find a best fit amplitude of \ln (D_A^2\Ysz/CY_X) = -0.202 \pm 0.024 at the
pivot point CY_X=8\times 10^{-5} Mpc^2. This corresponds to a Ysz/Yx-ratio of
0.82\pm 0.024, in good agreement with X-ray expectations after including the
effects of gas clumping. The slope of the relation is \alpha=0.916\pm 0.032,
consistent with unity at \approx 2.3\sigma. We are unable to detect intrinsic
scatter, and find no evidence that the scaling relation depends on cluster
dynamical state
Halo Model Analysis of Cluster Statistics
We use the halo model formalism to provide expressions for cluster abundances
and bias, as well as estimates for the correlation matrix between these
observables. Off-diagonal elements due to scatter in the mass tracer scaling
with mass are included, as are observational effects such as biases/scatter in
the data, detection rates (completeness), and false detections (purity). We
apply the formalism to a hypothetical volume limited optical survey where the
cluster mass tracer is chosen to be the number of member galaxies assigned to a
cluster. Such a survey can strongly constrain
(), the power law index where
(), and perhaps even
the Hubble parameter (). We find cluster abundances and
bias not well suited for constraining or the amplitude . We
also find that without bias information and are degenerate,
implying constraints on the former are strongly dependent on priors used for
the latter and vice-versa. The degeneracy stems from an intrinsic scaling
relation of the halo mass function, and hence it should be present regardless
of the mass tracer used in the survey.Comment: 27 pages, 11 figures, references adde
The Impact of Baryonic Cooling on Giant Arc Abundances
Using ray tracing for simple analytic profiles, we demonstrate that the
lensing cross section for producing giant arcs has distinct contributions due
to arcs formed through image distortion only, and arcs form from the merging of
two or three images. We investigate the dependence of each of these
contributions on halo ellipticity and on the slope of the density profile, and
demonstrate that at fixed Einstein radius, the lensing cross section increases
as the halo profile becomes steeper. We then compare simulations with and
without baryonic cooling of the same cluster for a sample of six clusters, and
demonstrate that cooling can increase the overall abundance of giant arcs by
factors of a few. The net boost to the lensing probability for individual
clusters is mass dependent, and can lower the effective low mass limit of
lensing clusters. This last effect can potentially increase the number of
lensing clusters by an extra 50%. While the magnitude of these effects may be
overestimated due to the well known overcooling problem in simulations, it is
evident that baryonic cooling has a non-negligible impact on the expected
abundance of giant arcs, and hence cosmological constraints from giant arc
abundances may be subject to large systematic errors.Comment: ApJ Submitte
Concordance Cosmology?
We propose a new intuitive metric for evaluating the tension between two
experiments, and apply it to several data sets. While our metric is
non-optimal, if evidence of tension is detected, this evidence is robust and
easy to interpret. Assuming a flat CDM cosmological model, we find
that there is a modest tension between the DES Year 1 results and
the measurements of the Cosmic Microwave Background (CMB). This
tension is driven by the difference between the amount of structure observed in
the late-time Universe and that predicted from fitting the data,
and appears to be unrelated to the tension between and local
esitmates of the Hubble rate. In particular, combining DES, Baryon Acoustic
Oscillations (BAO), Big-Bang Nucleosynthesis (BBN), and supernovae (SNe)
measurements recovers a Hubble constant and sound horizon consistent with , and in tension with local distance-ladder measurements. If the
tension between these various data sets persists, it is likely that reconciling
current data will require breaking the flat CDM model in
at least two different ways: one involving new physics in the early Universe,
and one involving new late-time Universe physics.Comment: 8 pages. 5 figure
Halo Exclusion Criteria Impacts Halo Statistics
Every halo finding algorithm must make a critical yet relatively arbitrary
choice: it must decide which structures are parent halos, and which structures
are sub-halos of larger halos. We refer to this choice as .
We demonstrate that the choice of percolation impacts the statistical
properties of the resulting halo catalog. Specifically, we modify the
halo-finding algorithm to construct four different halo
catalogs from the same simulation data, each with identical mass definitions,
but different choice of percolation. The resulting halos exhibit significant
differences in both halo abundance and clustering properties. Differences in
the halo mass function reach for halos of mass , larger than the few percent precision necessary for current
cluster abundance experiments such as the Dark Energy Survey. Comparable
differences are observed in the large-scale clustering bias, while differences
in the halo--matter correlation function reach on translinear scales.
These effects can bias weak-lensing estimates of cluster masses at a level
comparable to the statistical precision of current state-of-the-art
experiments.Comment: 8 pages, 6 figure
Weak Lensing Peak Finding: Estimators, Filters, and Biases
Large catalogs of shear-selected peaks have recently become a reality. In
order to properly interpret the abundance and properties of these peaks, it is
necessary to take into account the effects of the clustering of source
galaxies, among themselves and with the lens. In addition, the preferred
selection of lensed galaxies in a flux- and size-limited sample leads to
fluctuations in the apparent source density which correlate with the lensing
field (lensing bias). In this paper, we investigate these issues for two
different choices of shear estimators which are commonly in use today:
globally-normalized and locally-normalized estimators. While in principle
equivalent, in practice these estimators respond differently to systematic
effects such as lensing bias and cluster member dilution. Furthermore, we find
that which estimator is statistically superior depends on the specific shape of
the filter employed for peak finding; suboptimal choices of the
estimator+filter combination can result in a suppression of the number of high
peaks by orders of magnitude. Lensing bias generally acts to increase the
signal-to-noise \nu of shear peaks; for high peaks the boost can be as large as
\Delta \nu ~ 1-2. Due to the steepness of the peak abundance function, these
boosts can result in a significant increase in the abundance of shear peaks. A
companion paper (Rozo et al., 2010) investigates these same issues within the
context of stacked weak lensing mass estimates.Comment: 11 pages, 8 figures; comments welcom
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