Cloudy-Maraston

Stellar population synthesis models serve as indispensable tools for unraveling the intricate narrative of galaxy evolution, and allow physical parameters, such as stellar mass and stellar formation rate, to be inferred from spectrophotometric data through fitting. They are also key for linking physical properties in theoretical models to their forward modelled emission. The advent of the James Webb Space Telescope (JWST) has ushered in an era of abundant observations of young stellar populations at high redshift, characterized by strong emission lines, motivating us to integrate nebular emission into the M13 stellar population model.

This motivated me to use the photoionisation code Cloudy to add nebular emission new stellar population models by Maraston. I compared these to recent data by JWST and found that different modelling assumptions in stellar population models lead to very different predictions for the strength of the [O III] line which can lead to qualitative errors and highlights the need for careful model selection when interpreting observations.

Simulation-Based Inference

Traditional approaches to parameter inference rely on explicit likelihood functions, which are often intractable in complex astrophysical models. Simulation-Based Inference (SBI) offers a powerful alternative by directly learning the mapping between simulated data and model parameters using machine learning. This framework allows one to extract posteriors even when the likelihood is unknown, by training neural networks on forward-modeled simulations.

I use SBI techniques such as neural density estimation to constrain physical parameters from simulated galaxy spectra and photometry. By integrating simulation pipelines with inference frameworks, I can efficiently explore parameter spaces, quantify uncertainties, and connect theoretical models more directly to observations.

Working with Simulations and Semi-Analytic Models

To interpret observed galaxy populations, I combine stellar and AGN SED models with cosmological simulations and semi-analytic models (SAMs) of galaxy formation. These tools bridge small-scale physical processes, such as star formation and feedback, with large-scale structure formation in the Universe.