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Register a new userPreProFit -- Pressure Profile Fitter for galaxy clusters
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Galaxy cluster analyses based on high-resolution observations of the Sunyaev-Zeldovich (SZ) effect have become common in the last decade. We present PreProFit, the first publicly available code designed to fit the pressure profile of galaxy clusters from SZ data. PreProFit is based on a Bayesian forward-modelling approach, allows the analysis of data coming from different sources, adopts a flexible parametrization for the pressure profile, and fits the model to the data accounting for Abel integral, beam smearing, and transfer function filtering. PreProFit is computationally efficient, is extensively documented, has been released as an open source Python project, and was developed to be part of a joint analysis of X-ray and SZ data on galaxy clusters. PreProFit returns $chi^2$, model parameters and uncertainties, marginal and joint probability contours, diagnostic plots, and surface brightness radial profiles. PreProFit also allows the use of analytic approximations for the beam and transfer functions useful for feasibility studies.
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We present ProFit, a new code for Bayesian two-dimensional photometric galaxy profile modelling. ProFit consists of a low-level C++ library (libprofit), accessible via a command-line interface and documented API, along with high-level R (ProFit) and Python (PyProFit) interfaces (available at github.com/ICRAR/ libprofit, github.com/ICRAR/ProFit, and github.com/ICRAR/pyprofit respectively). R ProFit is also available pre-built from CRAN, however this version will be slightly behind the latest GitHub version. libprofit offers fast and accurate two- dimensional integration for a useful number of profiles, including Sersic, Core-Sersic, broken-exponential, Ferrer, Moffat, empirical King, point-source and sky, with a simple mechanism for adding new profiles. We show detailed comparisons between libprofit and GALFIT. libprofit is both faster and more accurate than GALFIT at integrating the ubiquitous Serrsic profile for the most common values of the Serrsic index n (0.5 < n < 8). The high-level fitting code ProFit is tested on a sample of galaxies with both SDSS and deeper KiDS imaging. We find good agreement in the fit parameters, with larger scatter in best-fit parameters from fitting images from different sources (SDSS vs KiDS) than from using different codes (ProFit vs GALFIT). A large suite of Monte Carlo-simulated images are used to assess prospects for automated bulge-disc decomposition with ProFit on SDSS, KiDS and future LSST imaging. We find that the biggest increases in fit quality come from moving from SDSS- to KiDS-quality data, with less significant gains moving from KiDS to LSST.
Much of the progress made in time-domain astronomy is accomplished by relating observational multi-wavelength time series data to models derived from our understanding of physical laws. This goal is typically accomplished by dividing the task in two: collecting data (observing), and constructing models to represent that data (theorizing). Owing to the natural tendency for specialization, a disconnect can develop between the best available theories and the best available data, potentially delaying advances in our understanding new classes of transients. We introduce MOSFiT: the Modular Open-Source Fitter for Transients, a Python-based package that downloads transient datasets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light curve fits to those datasets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT is designed to help bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result. As large-scale surveys such as LSST discover entirely new classes of transients, tools such as MOSFiT will be critical for enabling rapid comparison of models against data in statistically consistent, reproducible, and scientifically beneficial ways.
We describe a new open source package for calculating properties of galaxy clusters, including NFW halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density $Sigma(R)$ and differential surface mass density $DeltaSigma(R)$ profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo (Ford 2016). Full documentation, source code, and installation instructions are available at http://jesford.github.io/cluster-lensing/.
Ram-pressure stripping by the gaseous intra-cluster medium has been proposed as the dominant physical mechanism driving the rapid evolution of galaxies in dense environments. Detailed studies of this process have, however, largely been limited to relatively modest examples affecting only the outermost gas layers of galaxies in nearby and/or low-mass galaxy clusters. We here present results from our search for extreme cases of gas-galaxy interactions in much more massive, X-ray selected clusters at $z>0.3$. Using Hubble Space Telescope snapshots in the F606W and F814W passbands, we have discovered dramatic evidence of ram-pressure stripping in which copious amounts of gas are first shock compressed and then removed from galaxies falling into the cluster. Vigorous starbursts triggered by this process across the galaxy-gas interface and in the debris trail cause these galaxies to temporarily become some of the brightest cluster members in the F606W passband, capable of outshining even the Brightest Cluster Galaxy. Based on the spatial distribution and orientation of systems viewed nearly edge-on in our survey, we speculate that infall at large impact parameter gives rise to particularly long-lasting stripping events. Our sample of six spectacular examples identified in clusters from the Massive Cluster Survey, all featuring $M_{rm F606W}<-$21 mag, doubles the number of such systems presently known at $z>0.2$ and facilitates detailed quantitative studies of the most violent galaxy evolution in clusters.
We fit a functional form for a universal ICM entropy profile to the scaled entropy profiles of a catalogue of X-ray galaxy cluster outskirts results, which are all relaxed cool core clusters at redshift below 0.25. We also investigate the functional form suggested by Lapi et al. and Cavaliere et al. for the behaviour of the entropy profile in the outskirts and find it to fit the data well outside 0.3r200 . We highlight the discrepancy in the entropy profile behaviour in the outskirts between observations and the numerical simulations of Burns et al., and show that the entropy profile flattening due to gas clumping calculated by Nagai & Lau is insufficient to match observations, suggesting that gas clumping alone cannot be responsible for all of the entropy profile flattening in the cluster outskirts. The entropy profiles found with Suzaku are found to be consistent with ROSAT, XMM-Newton and Planck results.
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