On the stellar kinematics and mass of the Virgo ultradiffuse galaxy VCC 1287

Here, we present a kinematical analysis of the Virgo cluster ultradiffuse galaxy (UDG) VCC 1287 based on data taken with the Keck Cosmic Web Imager (KCWI). We confirm VCC 1287's association both with the Virgo cluster and its globular cluster (GC) system, measuring a recessional velocity of 1116 ± 2 km s-1. We measure a stellar velocity dispersion (19 ± 6 km s-1) and infer both a dynamical mass ($1.11^{+0.81}_{-0.81} \times 10^{9} \ \mathrm{M_{\odot }}$) and mass-To-light ratio (M/L) ($13^{+11}_{-11}$) within the half-light radius (4.4 kpc). This places VCC 1287 slightly above the well-established relation for normal galaxies, with a higher M/L for its dynamical mass than normal galaxies. We use our dynamical mass, and an estimate of GC system richness, to place VCC 1287 on the GC number-dynamical mass relation, finding good agreement with a sample of normal galaxies. Based on a total halo mass derived from GC counts, we then infer that VCC 1287 likely resides in a cored or low-concentration dark matter halo. Based on the comparison of our measurements to predictions from simulations, we find that strong stellar feedback and/or tidal effects are plausibly the dominant mechanisms in the formation of VCC 1287. Finally, we compare our measurement of the dynamical mass with those for other UDGs. These dynamical mass estimates suggest relatively massive haloes and a failed galaxy origin for at least some UDGs.The Observatory was made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Maunakea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. We thank the staff of the W. M. Keck Observatory for their assistance in using the telescope and wish to express particular gratitude to Luca Rizzi in helping us fix issues that presented themselves. We have also made heavy use of a number of open source packages in the creation of this work. We therefore wish to thank contributors to ASTROPY (Price-Whelan et al. 2018), SCIPY (Virtanen et al. 2020), SCIKIT–LEARN (Pedregosa et al. 2011), NUMPY (Oliphant 2019), SEABORN (Waskom et al. 2017), CORNER (Foreman-Mackey 2016), and EMCEE (Foreman-Mackey et al. 2013) for making their software publicly available. JSG acknowledges financial support received through a Swinburne University Postgraduate Research Award throughout the creation of this work. AFM has received financial support through the Post-doctoral Junior Leader Fellowship Programme from ‘La Caixa’ Banking Foundation (LCF/BQ/LI18/11630007). AJR was supported by National Science Foundation grant AST-1616710, and as a Research Corporation for Science Advancement Cottrell Scholar. JPB gratefully acknowledges support from National Science foundation grants AST-1518294 and AST-1616598