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Speaker: Benjamin Metha, University of MelbourneDate/Time: November 1, 2024 / 12 noon central - 12:30pm central.Location: NCSA, 1040.Zoom: https://illinois.zoom.us/j/82318062756?pwd=M3g1MFF6cytsOWFEbmU0UW1XWVoxQT09Title: Explorations into Chemical Inhomogeneities in Galaxies across Cosmic TimeAbstract: The modern generation of telescopes are sharp enough to observe chemical inhomogeneities in the interstellar medium of galaxies on a range of spatial scales, allowing us to directly see the small-scale processes that play a critical role in shaping how galaxies grow and evolve. In this talk, I present a selection of novel statistical techniques that can be used to model galaxy data, characterise the spatial chemical distribution of galaxies, and make meaningful comparisons to both analytical and simulated models. For well-resolved data (finer than ~200 pc per pixel), I introduce the geostatistical modelling framework that allows the multiscale metallicity structure of galaxies to be modelled and key physical parameters to be extracted. For data that is more coarsely resolved, I demonstrate the power of forward-modelling to fit more accurate metallicity gradients, as well as asymmetrical models that capture more details on a galaxy’s evolutionary history. Finally, I discuss a way to infer the presence of metallicity variations in unresolved sources by combining absorption and emission-based spectroscopy in host galaxies of gamma-ray bursts. These results shed light on the evolution of the interstellar medium of galaxies throughout cosmic time, and hold great promise for use in the analysis of future data sets.
Speaker: Ben Boyd, University of CambridgeDate/Time: November 1, 2024 / 12:30pm - 1:00pm central.Location: NCSA, 1040.Zoom: https://illinois.zoom.us/j/82318062756?pwd=M3g1MFF6cytsOWFEbmU0UW1XWVoxQT09Title: Hierarchical Bayesian Modelling for White Dwarf Calibration and Supernova CosmologyAbstract: Hierarchical Bayesian models provide a powerful framework for addressing complex problems in astrophysics by integrating multiple sources of uncertainty to infer population-level information. In this talk, I will present two applications of hierarchical Bayesian modelling to distinct astrophysical problems.The first model involves the spectrophotometric calibration of 32 faint DA white dwarf (DAWD) standards alongside the three CALSPEC primary standards across a broad wavelength range (1100 Å to 1.8 μm). By jointly inferring photometric zeropoints and WD parameters using both photometric and spectroscopic data, the model reaches sub-percent precision. This allows for the correction of instrument-dependent variations, such as HST cycle-dependent zeropoints and count rate non-linearity and supports population-level dust analysis for validating priors. The results are critical for ensuring precise calibration of upcoming surveys like the Vera Rubin Observatory’s LSST and the Nancy Grace Roman and Euclid surveys.The second model addresses the challenge of Malmquist bias in cosmological distance estimation using Type Ia Supernovae (SNe Ia). This selection effect skews detected samples towards brighter SNe at higher redshifts, leading to biased distance measurements and incorrect cosmological parameter constraints if untreated. I will outline a novel hierarchical Bayesian approach that combines simulation-based inference to correct for this bias. Our method generalizes well to real survey selection effects, providing more accurate estimates of cosmological parameters where traditional analytical corrections fail.