The SLUGGS Survey: a catalog of over 4000 globular cluster radial velocities in 27 nearby early-type galaxies

Abstract

Here we present positions and radial velocities for over 4000 globular clusters (GCs) in 27 nearby early-type galaxies from the SLUGGS survey. The SLUGGS survey is designed to be representative of elliptical and lenticular galaxies in the stellar mass range 10 $<$ log M${}_{\ast }$/M${}_{\odot }$ $<$ 11.7. The data have been obtained over many years, mostly using the very stable multi-object spectrograph DEIMOS on the Keck II 10m telescope. Radial velocities are measured using the calcium triplet lines with a velocity accuracy of $\pm$ 10-15 km/s. We use phase space diagrams (i.e. velocity--position diagrams) to identify contaminants such as foreground stars and background galaxies, and to show that the contribution of GCs from neighboring galaxies is generally insignificant. Likely ultra-compact dwarfs are tabulated separately. We find that the mean velocity of the GC system is close to that of the host galaxy systemic velocity, indicating that the GC system is in overall dynamical equilibrium within the galaxy potential. We also find that the GC system velocity dispersion scales with host galaxy stellar mass in a similar manner to the Faber-Jackson relation for the stellar velocity dispersion. Publication of these GC radial velocity catalogs should enable further studies in many areas, such as GC system substructure, kinematics, and host galaxy mass measurements.

16 pages, accepted for publication in the Astronomical Journal, online data files can be found at sluggs.swin.edu.au

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• What to expect when using globular clusters as tracers of the total mass distribution in Milky Way-mass galaxies
• Globular clusters in the stellar stream surrounding the Milky Way analog NGC 5907
• Dark Coincidences: Small-Scale Solutions with Refracted Gravity and MOND
• The present-day globular cluster kinematics of lenticular galaxies from the E-MOSAICS simulations and their relation to the galaxy assembly histories
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• Using Machine Learning to Identify Extragalactic Globular Cluster Candidates from Ground-Based Photometric Surveys of M87