English

Modeling Tidal Streams and Tidal Tails around Galaxies Using Deep Wendelstein Imaging Data

Astrophysics of Galaxies 2025-02-21 v1

Abstract

From the Λ\Lambda cold dark matter paradigm it is expected that galaxies merge and grow in their environments. These processes form various tidal features depending on the merger mass ratio, orbital parameters, and gas richness. We inspected 170 gg'-band Abell cluster observations from the 2.1m Fraunhofer-Teleskop Wendelstein and identify 111 of such features from which we select nine streams and five tails. A fast and innovative technique was developed for determining their photometric properties. The model is a Gaussian, including higher-order moments to describe the light profile in slices perpendicular to the elongation direction. From these models, FWHM apertures are generated. The method was developed, tested, and applied on the selected features and corresponding gg- and rr-band data from the Legacy Survey DR10. Regarding the novel modeling approach, we can measure surface and total brightnesses with precisions of 4% and 7%, respectively. Mean stream width precision, which also translates to the mean ReR_\mathrm{e} along the feature is on average within 3% uncertainty. The measured streams have on average a surface brightness of 26.25\sim26.25 gg' mag arcsec2^{-2} and are dimmer than the tails in our sample (25.14\sim25.14 gg' mag arcsec2^{-2}). We infer that the progenitors of our streams can come from dwarfs, early-type galaxies or disks, based on the streams structural parameters. Furthermore, brightnesses and colors of the streams and tails are consistent with those of galaxies that populate the red sequence in the Coma cluster within 2σ2\sigma.

Keywords

Cite

@article{arxiv.2502.14531,
  title  = {Modeling Tidal Streams and Tidal Tails around Galaxies Using Deep Wendelstein Imaging Data},
  author = {Jan-Niklas Pippert and Matthias Kluge and Ralf Bender},
  journal= {arXiv preprint arXiv:2502.14531},
  year   = {2025}
}

Comments

22 pages, 23 figures

R2 v1 2026-06-28T21:51:18.697Z