FLAGS II: Constraining Galaxy Formation Models with Dimensionality Reduction of Direct Observables
TL;DR
The embedded position of each galaxy can be identified $>100$ times faster than inferring its properties with Bayesian SED fitting, making this approach an ideal alternative for deriving statistical model constraints from large surveys such as LSST and Euclid, and performing simulation-based inference with CAMELS.
Abstract
Comparisons between observations of galaxies and theoretical predictions are regularly performed using physical properties, which are inferred by the often slow and biased process of SED fitting. Forward modelling facilitates a reliable alternative, whereby models are evaluated using direct observables alone. However, these datasets become high-dimensional when collating observations from multiple telescopes, leading to sparse sampling, memory intensity and visualisation difficulties. We show that 2D embeddings of JWST and HST photometric fluxes, constructed using the non-linear dimensionality reduction algorithm UMAP, preserve sufficient information to differentiate between five models. Using a simple $\chi^{2}$-like metric, we show that JAGUAR reproduces the population of bright galaxies $(m_{\mathrm{AB}}<26)$ in GOODS-S six times as well as SC-SAM and twelve times as well as SAGE. By adjusting the hyperparameters, we quantify how well each model replicates the distribution of SED shapes. The template SED approach of SPRITZ and the lack of photoionisation in SAGE cause significant discrepancies, highlighting the importance of comprehensive forward modelling. The embedded position of each galaxy can be identified $>100$ times faster than inferring its properties with Bayesian SED fitting, making this approach an ideal alternative for deriving statistical model constraints from large surveys such as LSST and Euclid, and performing simulation-based inference with CAMELS.