Skip to main content
SearchLoginLogin or Signup

Multi-phase/multi-sector ISM modeling techniques for unresolved galaxies: inferring the influence of compact objects in dwarf galaxies

Presentation #145.10 in the session Dwarf and Irregular Galaxies — iPoster Session.

Published onJun 29, 2022
Multi-phase/multi-sector ISM modeling techniques for unresolved galaxies: inferring the influence of compact objects in dwarf galaxies

As the number of spectroscopic tracers available in high-z galaxies increases, the difficult task of inferring the properties of the galaxy and its interstellar medium (ISM) requires more than ever detailed observations of nearby galaxies, in particular low-metallicity star-forming dwarf galaxies, as well as state-of-the-art models that reflect the ISM complexity (distribution of phases, distribution of matter).

Through the lens of the Dwarf Galaxy Survey (DGS; Madden et al. 2013) observed with Spitzer and Herschel, I will review the various iterations in the methods used to extract topological information and ISM physical conditions from a suite of spatially- and spectrally unresolved optical and infrared lines and how the method can be adapted and applied to high-z galaxies.

The latest iteration developed in our group consists in using Bayesian statistics in order to solve known issues with deterministic methods related, e.g., to the existence of multiple solutions, correlated parameters and observations, accounting for large number of ISM components etc... The current method makes it possible to infer physical parameters to be explored in a grid and provides statistical predictions for unobserved tracers or other, secondary, physical parameters (e.g., masses).

I will illustrate this approach by analyzing the most metal-poor star-forming dwarf galaxy known, IZw18, reproducing and extending on the results of Lebouteiller et al. (2017) where we show that a compact object dominates the neutral gas heating with important implications on the origin of [CII] and the distribution of molecular gas.

Finally I will present a prospective of future iterations using Machine-Learning techniques and describing the integrated emission as the result of selection effects from a vast ensemble of clouds.

Comments
0
comment

No comments here