Measurement of the Relativistic Sunyaev–Zeldovich Correction in RX J1347.5-1145

Co-led with Victoria Butler, Accepted to AJ, link here.

Abstract: We present a measurement of the relativistic corrections to the thermal Sunyaev–Zel'dovich (SZ) effect spectrum, the rSZ effect, toward the massive galaxy cluster RX J1347.5-1145 by combining submillimeter images from Herschel-SPIRE with millimeter wavelength Bolocam maps. Our analysis simultaneously models the SZ effect signal, the population of cosmic infrared background galaxies, and the galactic cirrus dust emission in a manner that fully accounts for their spatial and frequency-dependent correlations. Gravitational lensing of background galaxies by RX J1347.5-1145 is included in our methodology based on a mass model derived from the Hubble Space Telescope observations. Utilizing a set of realistic mock observations, we employ a forward modeling approach that accounts for the non-Gaussian covariances between the observed astrophysical components to determine the posterior distribution of SZ effect brightness values consistent with the observed data. We determine a maximum a posteriori (MAP) value of the average Comptonization parameter of the intracluster medium (ICM) within R2500 to be 〈y〉2500 = 1.56 × 10−4, with corresponding 68% credible interval [1.42, 1.63] × 10−4, and a MAP ICM electron temperature of 〈Tsz〉2500 = 22.4 keV with 68% credible interval spanning [10.4, 33.0] keV. This is in good agreement with the pressure-weighted temperature obtained from Chandra X-ray observations, 〈Tx,pw〉2500 = 17.4 ± 2.3 keV. We aim to apply this methodology to comparable existing data for a sample of 39 galaxy clusters, with an estimated uncertainty on the ensemble mean 〈Tsz〉2500 at the ≃ 1 keV level, sufficiently precise to probe ICM physics and to inform X-ray temperature calibration.

Non-linear 3D Cosmic Web Simulation with Heavy-Tailed Generative Adversarial Networks

In collaboration with Philippe Berger and George Stein. Accepted to Physical Review D, arXiv link here.

Many inference problems in cosmology incorporate information from simulations when making comparisons to observational data. Comparing data to simulations (e.g., N-body, hydrodynamical, etc.) can be challenging when the data are high-dimensional and when generating the simulations is computationally expensive. Some summary statistics can be analytically derived (the power spectrum being one example), but for higher order statistics and covariances these analytical prescriptions often have limitations. I am interested in the ways deep generative modeling can be used to bridge the gap between expensive simulations and robust cosmological inference.

My collaborators and I investigated how well generative adversarial networks (GANs) could emulate evolved dark matter density fields trained on ensembles of N-body simulations. Deep learning methods excel at modeling non-linear data, so by emulating directly to the data space, summary statistics like the power spectrum and bispectrum can be predicted well into the small-scale, non-linear regime where analytic methods fail but where there is often more constraining power. In addition to reproducing the two- and three-point statistics of GADGET dark matter simulations (and their covariances), we demonstrated the capability for these models to interpolate in redshift space. Work by others following this publication has extended this deep generative modeling approach to interpolate cosmological parameters and model other fields such as those from hydrodynamical simulations.