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 $\sigma_8$ ($\Delta\sigma_8\approx 0.05$), the power law index $\alpha$ where $= 1+(m/M_1)^\alpha$ ($\Delta\alpha\approx0.03$), and perhaps even the Hubble parameter ($\Delta h\approx 0.07$). We find cluster abundances and bias not well suited for constraining $\Omega_m$ or the amplitude $M_1$. We also find that without bias information $\sigma_8$ and $\alpha$ 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 $\Lambda$CDM cosmological model, we find that there is a modest $2.2\sigma$ tension between the DES Year 1 results and the ${\it Planck}$ 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 ${\it Planck}$ data, and appears to be unrelated to the tension between ${\it Planck}$ 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 ${\it Planck}$, and in tension with local distance-ladder measurements. If the tension between these various data sets persists, it is likely that reconciling ${\it all}$ current data will require breaking the flat $\Lambda$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 ${\it percolation}$. We demonstrate that the choice of percolation impacts the statistical properties of the resulting halo catalog. Specifically, we modify the halo-finding algorithm ${\tt ROCKSTAR}$ 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 $10\%$ for halos of mass $10^{13}\ h^{-1}\ {\rm M_{\odot}}$, 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 $40\%$ 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