Weak lensing calibration of mass bias in the REFLEX+BCS X-ray galaxy cluster catalogue

The use of large, X-ray selected galaxy cluster catalogues for cosmological analyses requires a thorough understanding of the X-ray mass estimates. Weak gravitational lensing is an ideal method to shed light on such issues, due to its insensitivity to the cluster dynamical state. We perform a weak lensing calibration of 166 galaxy clusters from the REFLEX and BCS cluster catalogue and compare our results to the X-ray masses based on scaled luminosities from that catalogue. To interpret the weak lensing signal in terms of cluster masses, we compare the lensing signal to simple theoretical Navarro-Frenk-White models and to simulated cluster lensing profiles, including complications such as cluster substructure, projected large-scale structure, and Eddington bias. We find evidence of underestimation in the X-ray masses, as expected, with $\langle M_{\mathrm{X}}/M_{\mathrm{WL}}\rangle = 0.75 \pm 0.07$ stat. $\pm 0.05$ sys. for our best-fit model. The biases in cosmological parameters in a typical cluster abundance measurement that ignores this mass bias will typically exceed the statistical errors.Comment: 13 pages, 5 figures. Revised to address referee comment

Future constraints on halo thermodynamics from combined Sunyaev-Zel'dovich measurements

The improving sensitivity of measurements of the kinetic Sunyaev-Zel'dovich (SZ) effect opens a new window into the thermodynamic properties of the baryons in halos. We propose a methodology to constrain these thermodynamic properties by combining the kinetic SZ, which is an unbiased probe of the free electron density, and the thermal SZ, which probes their thermal pressure. We forecast that our method constrains the average thermodynamic processes that govern the energetics of galaxy evolution like energetic feedback across all redshift ranges where viable halos sample are available. Current Stage-3 cosmic microwave background (CMB) experiments like AdvACT and SPT-3G can measure the kSZ and tSZ to greater than 100$\sigma$ if combined with a DESI-like spectroscopic survey. Such measurements translate into percent-level constraints on the baryonic density and pressure profiles and on the feedback and non-thermal pressure support parameters for a given ICM model. This in turn will provide critical thermodynamic tests for sub-grid models of feedback in cosmological simulations of galaxy formation. The high fidelity measurements promised by the next generation CMB experiment, CMB-S4, allow one to further sub-divide these constraints beyond redshift into other classifications, like stellar mass or galaxy type.Comment: 11 pages, 3 figures, Accepted to JCA

Adaptive kernel estimation for enhanced filtering and pattern classification of magnetic resonance imaging: novel techniques for evaluating the biomechanics and pathologic conditions of the lumbar spine

This dissertation investigates the contribution the lumbar spine musculature has on etiological and pathogenic characteristics of low back pain and lumbar spondylosis. This endeavor necessarily required a two-step process: 1) design of an accurate post-processing method for extracting relevant information via magnetic resonance images and 2) determine pathological trends by elucidating high-dimensional datasets through multivariate pattern classification. The lumbar musculature was initially evaluated by post-processing and segmentation of magnetic resonance (MR) images of the lumbar spine, which characteristically suffer from nonlinear corruption of the signal intensity. This so called intensity inhomogeneity degrades the efficacy of traditional intensity-based segmentation algorithms. Proposed in this dissertation is a solution for filtering individual MR images by extracting a map of the underlying intensity inhomogeneity to adaptively generate local estimates of the kernel’s optimal bandwidth. The adaptive kernel is implemented and tested within the structure of the non-local means filter, but also generalized and extended to the Gaussian and anisotropic diffusion filters. Testing of the proposed filters showed that the adaptive kernel significantly outperformed their non-adaptive counterparts. A variety of performance metrics were utilized to measure either fine feature preservation or accuracy of post-processed segmentation. Based on these metrics the adaptive filters proposed in this dissertation significantly outperformed the non-adaptive versions. Using the proposed filter, the MR data was semi-automatically segmented to delineate between adipose and lean muscle tissues. Two important findings were reached utilizing this data. First, a clear distinction between the musculature of males and females was established that provided 100% accuracy in being able to predict gender. Second, degenerative lumbar spines were accurately predicted at a rate of up to 92% accuracy. These results solidify prior assumptions made regarding sexual dimorphic anatomy and the pathogenic nature of degenerative spine disease

Cluster and Protocluster Mass Estimation and Determination of their Dynamical States

The identification of high-redshift clusters and proto-clusters is a rapidly growing field. For the purposes of cosmology and galaxy formation characterizing the masses of these halos is critical. I will review methods for measuring proto-cluster masses and some of the systematic uncertainties associated with these estimates. I will present the exciting new opportunities to find and measure proto-cluster masses that future millimeter-wave surveys will provide