Overview
Extracting a black hole mass from observations is fundamentally a forward-modelling problem: a physical model is convolved with the instrument response and the atmosphere, compared with the data, and explored statistically. End-to-end instrument simulators make it possible to ask, before any telescope time is awarded, whether a given target can be measured at all, what exposure time and spectral setup are required, and how the recovered parameters are biased by point-spread function structure, spectral resolution and signal-to-noise. The same machinery is needed after the fact to propagate realistic uncertainties. This makes reproducible, well-tested code an integral part of the science rather than a side activity.
My Research
I work in Python with numpy, astropy, matplotlib and pandas, and in Matlab, and I develop pipelines that decompose AGN and stellar light in JWST data, generate mock HARMONI integral-field datacubes with HSIM and MICADO images with SimCADO, and fit kinematic models with KinMS. With these tools I design observing strategies for detecting the kinematic signatures of intermediate-mass black holes within about 20 Mpc, assess the limitations of HARMONI for that science case, and forecast dynamical mass measurements of supermassive black holes in quiescent ellipticals beyond 100 Mpc.
Literature Review
Literature review for this theme is in preparation and will be added here.
Summary of Findings
A summary of key results and findings for this theme is in preparation and will be added here.