The mysterious globular cluster population of MATLAS-2019

¹ Instituto de Astrofísica de Canarias, c/ Vía Láctea s/n, 38205 La Laguna, Tenerife, Spain
² Departamento de Astrofísica, Universidad de La Laguna, 38206 La Laguna, Tenerife, Spain
³ Institute of Space Sciences (ICE, CSIC), Campus UAB, Carrer de Can Magrans, s/n, 08193 Barcelona, Spain

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 12 September 2025
Accepted: 10 December 2025

Abstract

MATLAS-2019 (also known as NGC5846-UDG1) has attracted significant attention due to the ongoing debate surrounding its globular cluster (GC) population, with several studies addressing the issue, yet reaching little consensus. For this paper, we took advantage of HST’s multi-wavelength coverage (F475W, F606W, and F814W observations) with the addition of deep u-band imaging from the Gran Telescopio Canarias (GTC), to perform the most detailed study and estimation to date of the GC population of the ultra-diffuse galaxy MATLAS-2019. The improved constraints provided by the combination of high spatial resolution and better coverage of the GC spectral energy distribution has allowed us to obtain a clean sample of GCs in this galaxy. We report a number of 33 ± 3 GCs in MATLAS-2019, supporting the previous lower estimates for this galaxy. The GC population of this galaxy is highly concentrated with ∼80% of the GCs inside the effective radius (R_e) of the galaxy, and the GC half-number radius R_e,GC is 0.7 × R_e. Using the GC-halo mass relation, we estimate a halo mass for MATLAS-2019 of (1.14 ± 0.1) × 10¹¹ M_⊙. The GC luminosity function and the distribution of effective radii of the GCs favour a distance to the galaxy of 20.0 ± 0.9 Mpc. In agreement with previous findings, we find that the distribution of GCs is highly asymmetric even though the distribution of stars in the galaxy is symmetric. This suggests that assumptions about the symmetry of the GC distribution may be incorrect when used to calculate the number of GCs with such low statistics.