Studying the Physical Properties of Tidal Features I. Extracting Morphological Substructure in CANDELS Observations and VELA Simulations

The role of major mergers in galaxy evolution remains a key open question. Existing empirical merger identification methods use non-parametric and subjective visual classifications which can pose systematic challenges to constraining merger histories. As a first step towards overcoming these challenges, we develop and share publicly a new Python-based software tool that identifies and extracts the flux-wise and area-wise significant contiguous regions from the model-subtracted "residual" images produced by popular parametric light-profile fitting tools (e.g., GALFIT). Using Hubble Space Telescope ($HST$) $H$-band single-S\'ersic residual images of $17$ CANDELS galaxies, we demonstrate the tool's ability to measure the surface brightness and improve the qualitative identification of a variety of common residual features (disk structures, spiral substructures, plausible tidal features, and strong gravitational arcs). We test our method on synthetic $HST$ observations of a $z\sim 1.5$ major merger from the VELA hydrodynamic simulations. We extract $H$-band residual features corresponding to the birth, growth, and fading of tidal features during different stages and viewing orientations at CANDELS depths and resolution. We find that the extracted features at shallow depths have noisy visual appearance and are susceptible to viewing angle effects. For a VELA $z\sim 3$ major merger, we find that James Webb Space Telescope NIRCam observations can probe high-redshift tidal features with considerable advantage over existing $HST$ capabilities. Further quantitative analysis of plausible tidal features extracted with our new software hold promise for the robust identification of hallmark merger signatures and corresponding improvements to merger rate constraints.
Comment: 18 pages, 11 Figures, and accepted for publication in MNRAS. The data products discussed in this paper along with the software are publicly available at https://github.com/AgentM-GEG/residual_feature_extraction