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 that 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-Sérsic residual images of 17 CANDELS galaxies, we demonstrate the tools ability to measure the surface brightness and improve the qualitative identification of a variety of common residual features (disc structures, spiral substructures, plausible tidal features, and strong gravitational arcs). We test our method on synthetic HST observations of a z ∼ 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 ∼ 3 major merger, we find that James Webb Space Telescope NIRCam observations can probe highredshift 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.
|Original language||English (US)|
|Number of pages||17|
|Journal||Monthly Notices of the Royal Astronomical Society|
|State||Published - Jun 1 2019|
Bibliographical noteFunding Information:
We thank Roberto Abraham for the constructive referee suggestions and feedback that improved this work. We thank Mark Brodwin, Gabriela Canalizo, Alexander De la Vega, Boris Häußler, Marc Huertas-Company, Kartheik Iyer, Marziye Jafariyazani, Erin Kado-Fong, Allison Kirkpatrick, David Koo, Peter Kurczynski, Bret Lehmer, Jennifer Lotz, Bahram Mobasher, Ripon Saha, and Xi-anzhong Zheng for their helpful suggestions. KBM, DHM, LDL, and RE acknowledge funding from the NASA Hubble Space Telescope Archival Research grant 15040. CPC, RE, LBF, LDL, and SET acknowledge support from the Missouri Consortium of NASA’s National Space Grant College and Fellowship Programme. KBM also acknowledges funding from the School of Graduate Studies (SGS) fellowship grant and the Ronald A. MacQuarrie graduate fellowship offered by the University of Missouri-Kansas City. DC acknowledges funding by the ERC Advanced Grant, STARLIGHT: Formation of the First Stars (project number 339177). This work is based on observations taken by the CANDELS Multi-Cycle Treasury Programme with the NASA/ESA HST, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for Programme number HST-GO-12060 was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Incorporated, under NASA contract NAS5-26555. The VELA simulations were performed at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory, and at NASA Advanced Supercomputing (NAS) at NASA Ames Research Center. This publication also made use of NASA’s Astrophysics Data System Bibliographic Services, TOPCAT (Tools for OPerations on Catalogues And Tables, Taylor 2005), the core PYTHON package for the astronomy community (ASTROPY 1.2.1; Astropy Collaboration et al. 2013), and collage maker (github.com/delimitry/collage maker).
© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.
- galaxies: evolution
- galaxies: high-redshift
- galaxies: statistics.