Characterizing the Conditional Galaxy Property Distribution Using Gaussian Mixture Models

Abstract

Line-intensity mapping (LIM) is a promising technique to constrain the global distribution of galaxy properties. To combine LIM experiments probing different tracers with traditional galaxy surveys and fully exploit the scientific potential of these observations, it is necessary to have a physically motivated modeling framework. As part of developing such a framework, in this work, we introduce and model the conditional galaxy property distribution (CGPD), i.e., the distribution of galaxy properties conditioned on the host halo mass and redshift. We consider five galaxy properties, including the galaxy stellar mass, molecular gas mass, galaxy radius, gas-phase metallicity, and star formation rate (SFR), which are important for predicting the emission lines of interest. The CGPD represents the full distribution of galaxies in the five-dimensional property space; many important galaxy distribution functions and scaling relations, such as the stellar mass function and SFR main sequence, can be derived from integrating and projecting it. We utilize two different kinds of cosmological galaxy simulations, a semi-analytic model and the IllustrisTNG hydrodynamic simulation, to characterize the CGPD and explore how well it can be represented using a Gaussian mixture model (GMM). We find that with just a few (approximately three) Gaussian components, a GMM can describe the CGPD of the simulated galaxies to high accuracy for both simulations. The CGPD can be mapped to LIM or other observables by constructing the appropriate relationship between galaxy properties and the relevant observable tracers, which will be discussed in future works.

The Global Structure of Molecular Clouds. I. Trends with Mass and Star Formation Rate

“…Changes in the underlying assumptions about the typical morphology of clouds, i.e. from spherical to cylindrical or filamentary, may alter the inference of the galaxy property distribution (Zhang et al 2023) from forthcoming emission line intensity mapping surveys (e.g. Pullen et al 2023).…”

“…The size and morphology explicitly enter these models, which are then summed to predict line luminosities as a function of galaxy properties (Popping et al 2019;Yang et al 2022). Changes in the underlying assumptions about the typical morphology of clouds, i.e., from spherical to cylindrical or filamentary, may alter the inference of the galaxy property distribution (Zhang et al 2023) from forthcoming emission line intensity mapping surveys (e.g., Pullen et al 2023).…”

Constraining the halo-ISM connection through multi-transition carbon monoxide line-intensity mapping

“…In devising suitable models of the high-redshift ISM, LIM signal forecasts will need to interface more with both semi-analytic models and hydrodynamical simulations that explicitly simulate processes of cloud formation and dissociation. Of particular value will be the use of Gaussian process emulators (as considered in this work, although hopefully employed with more sophistication) or Gaussian mixture models (cf., e.g., [78,79]) to inform halo models to 'paint' onto large-scale cosmological simulations, or dimensionality reduction techniques to gain insights into specific variables that describe the 'painting'. The advantage of some of these machine learning models will be the ability to characterise uncertainty in simplifying or extrapolating the fine-grain information, and the ability in principle to propagate these uncertainties all the way through to parameter inferences.…”

Cross-correlation Techniques to Mitigate the Interloper Contamination for Line Intensity Mapping Experiments

“…Line intensity mapping (LIM) is a promising approach that allows us to probe the three-dimensional (3D) structure of the Universe beyond the traditional galaxy-by-galaxy surveys and explore the collective properties of atomic and molecular emissions from the interstellar medium of galaxies (Visbal & Loeb 2010;Visbal et al 2011;Kovetz et al 2017;Bernal & Kovetz 2022). By indirectly tracing the cumulative radiation from multiple atomic and molecular emission lines, LIM offers a unique statistical view of large-scale structures, providing valuable insights into galaxy formation and evolution across cosmic time (Sun et al 2023;Zhang et al 2023). This technique has emerged as a tool for investigating the cosmic landscape across vast cosmic volumes and exploring the evolution of galaxies, intergalactic gas, and cosmic star formation (Bernal & Kovetz 2022; Z.…”