  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.
