Towards a higher mass for NGC1052-DF2: an analysis based on full distribution functions

TL;DR

This paper analyzes the kinematics of star clusters in NGC1052-DF2 using distribution functions, tidal stripping effects, and ΛCDM halo profiles to estimate its mass, challenging previous low-mass claims.

Contribution

It introduces a full distribution function approach to kinematic analysis, incorporating tidal stripping effects to better estimate the galaxy's mass.

Findings

High dynamical mass consistent with standard SHMR within uncertainties.

Tidal stripping reduces velocity range but mildly affects total mass estimates.

2σ upper limit on virial mass is significantly below the SHMR prediction.

Abstract

It is demonstrated that the kinematics of the 10 star clusters in NGC2052-DF2 is compatible with a high dynamical mass close to those implied by the standard stellar-to-halo-mass ratio (SHMR). The analysis relies on a convenient form for the distribution function (DF) of projected phase space data, capturing non-gaussian features in the spread of true velocities of the mass tracers. A key ingredient is tidal stripping by the gravity of the apparently nearby larger galaxy, NGC 1052. Tidal stripping decreases the range of velocities of mass tracers, while only mildly lowering the total mass inside the trimming radius $r_{tr}$. The analysis is performed assuming halo profiles consistent with simulations of the $\Lambda$CDM model. For the fiducial value $r_{tr}=10$ kpc, we find that the virial mass of the pre-trimmed halo is $M<1.6\times 10^{10}M_\odot$ at $2\sigma $ ($95\%$) and $M<8.6\times 10^{9}M_\odot$ at $1.64\sigma$ ($90\%$). For the mass within 10 kpc we obtain, $M_\mathrm{10kpc}<3.9\times 10^{9}M_\odot$ and $<2.9\times 10^{9}M_\odot$ at $2\sigma$ and $1.64\sigma$, respectively. The $2\sigma$ upper limit on the virial mass is roughly a factor of 3-5 below the mean SHMR relation.Taking $r_{tr}=20$ kpc, lowers the $2\sigma$ virial mass limits by a factor of $\sim 4 $, bringing our results closer to those of Wasserman et al. (2018) without their SHMR prior.