A New Identification Method and Sample of Counter-Rotating Disk Galaxies in SDSS-IV MaNGA DR17

TL;DR

This paper presents an automated method to identify counter-rotating disk galaxies in SDSS-IV MaNGA DR17, significantly increasing the known sample and enabling better statistical analysis of their properties and formation processes.

Contribution

The authors developed an automated pre-selection technique for CRDs, doubling the known sample size in MaNGA DR17 and facilitating large-scale statistical studies.

Findings

No significant difference in emission-line diagnostics between CRDs and control galaxies.

The new method efficiently identifies CRDs in large spectroscopic surveys.

CRDs do not show distinct photoionization signatures from typical early-type galaxies.

Abstract

Counter-Rotating Disk (CRD) galaxies have two co-spatial stellar disks rotating in opposite directions, and provide a rare opportunity to study external gas accretion and dynamical assembly processes. In the 16th data release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, only 64 CRDs were visually identified. Using this as a training sample, we developed an automated pre-selection method that reduces the number of galaxies requiring visual inspection by removing systems unlikely to host counter-rotation. Applying this method to MaNGA Data Release 17, we identified 126 confirmed CRDs and an additional 143 candidate galaxies, more than doubling the MaNGA CRD sample. With this extended sample, we analyzed their Baldwin, Phillips, and Terlevich (BPT) emission-line diagrams and compared them with a matched control sample of early-type galaxies (ETG). We found no statistically significant difference in photoionization sources between CRDs and the ETG control sample, indicating emission-line diagnostics cannot solely be used to identify counter-rotating disks, nor do they correspond to a distinct present-day photoionization signature. Our method facilitates efficient discovery of CRDs in large spectroscopic surveys, enabling stronger statistical studies of their formation and evolution.