Characterization of low surface brightness structures in annotated deep images

Abstract

Context. The identification and characterization of low surface brightness (LSB) stellar structures around galaxies such as tidal debris of ongoing or past collisions is essential to constrain models of galactic evolution. So far most efforts have focused on the numerical census of samples of varying sizes, either through visual inspection or more recently with deep learning. Detailed analyses including photometry have been carried out for a small number of objects, essentially because of the lack of convenient tools able to precisely characterize tidal structures around large samples of galaxies. Aims. Our goal is to characterize in detail, and in particular obtain quantitative measurements, of LSB structures identified in deep images of samples consisting of hundreds of galaxies. Methods. We developed an online annotation tool that enables contributors to delineate the shapes of diffuse extended stellar structures with precision, as well as artifacts or foreground structures. All parameters are automatically stored in a database which may be queried to retrieve quantitative measurements. We annotated LSB structures around 352 nearby massive galaxies with deep images obtained with the Canada-France-Hawaii Telescope as part of two large programs: Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) and Ultraviolet Near Infrared Optical Northern Survey (UNIONS)/Canada-France Imaging Survey (CFIS). Each LSB structure was delineated and labeled according to its likely nature: stellar shells, streams associated with a disrupted satellite, tails that formed in major mergers, ghost reflections, or cirrus. Results. From our database containing 8441 annotations, the area, size, median surface brightness, and distance to the host of 228 structures were computed. The results confirm the fact that tidal structures defined as streams are thinner than tails, as expected by numerical simulations. In addition, tidal tails appear to exhibit a higher surface brightness than streams (by about 1 mag), which may be related to different survival times for the two types of collisional debris. We did not detect any tidal feature fainter than 27.5 mag arcsec −2 , while the nominal surface brightness limits of our surveys range between 28.3 and 29 mag arcsec −2 , a difference that needs to be taken into account when estimating the sensitivity of future surveys to identify LSB structures. Conclusions. We compiled an annotation database of observed LSB structures around nearby massive galaxies including tidal features that may be used for quantitative analysis and as a training set for machine learning algorithms.

Massive Early-type Galaxies in the HSC-SSP: Flux Fraction of Tidal Features and Merger Rates

“…As shown in the literature (e.g., Kado-Fong et al 2018;Sola et al 2022), in observations, the surface brightness limit for a reliable tidal feature detection is always much shallower than the nominal depth of the images. This difference is due to various reasons, such as the apparent size of tidal features and methods used to estimate these limits.…”

“…The difference between i band and r band is probably due to different noise levels between these two bands and the intrinsic color of tidal features. Compared with Sola et al (2022), there seem to be so few tidal features with high surface brightness (μ r,i  25 mag arcsec −2 ) in our sample. The discrepancy in bright tidal features could be due to the following reasons: (1) ongoing mergers are excluded from our sample, in which bright features often occur; (2) incorrect segmentation of SExtractor leads to masks on these features; and (3) Sola et al (2022) did not perform model subtraction before measuring the surface brightness of tidal features.…”

“…Nowadays, with the aid of machine-learning techniques and numerical simulations, it is possible to infer the merging histories of galaxies from their integral properties alone (Eisert et al 2022). If the flux fractions or even the annotated maps of tidal features are used to train the networks, the performance of machine learning may get a pronounced improvement (Sola et al 2022; the Galaxy Cruise project 4 ).…”

A Stream Come True -- Connecting tidal tails, shells, streams, and planes with galaxy kinematics and formation history

“…To study tidal features in a more statistically significant way, LSB features have been systematically classified in galaxy surveys, such as MATLAS (Duc et al 2015;Bílek et al 2020;Sola et al 2022) or the Stellar Stream Legacy Survey (Martinez-Delgado et al 2021). The features are oftentimes identified through visual inspection, though fully automated approaches have been developed as well.…”

“…The features are oftentimes identified through visual inspection, though fully automated approaches have been developed as well. However, such automated methods can have issues with their sensitivity to LSB features (Sola et al 2022).…”