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Candidate Dark Galaxy-2 (CDG-2) is a potential dark galaxy consisting of four globular clusters (GCs) in the Perseus cluster, first identified in D. Li et al. through a sophisticated statistical method. The method searched for overdensities of GCs from a Hubble Space Telescope (HST) survey targeting Perseus. Using the same HST images and new imaging data from the Euclid survey, we report the detection of extremely faint but significant diffuse emission around the four GCs of CDG-2. We thus have exceptionally strong evidence that CDG-2 is a galaxy. This is the first galaxy detected purely through its GC population. Under the conservative assumption that the four GCs make up the entire GC population, preliminary analysis shows that CDG-2 has a total luminosity of L_V,gal = 6.2 ± 3.0 × 10^6 L_⊙ and a minimum GC luminosity of L_V,GC = 1.03 ± 0.2 × 10^6 L_⊙. Our results indicate that CDG-2 is one of the faintest galaxies having associated GCs, while at least ∼16.6% of its light is contained in its GC population. This ratio is likely to be much higher (∼33%) if CDG-2 has a canonical GC luminosity function (GCLF). In addition, if the previously observed GC-to-halo mass relations apply to CDG-2, it would have a minimum dark matter halo mass fraction of 99.94% to 99.98%. If it has a canonical GCLF, then the dark matter halo mass fraction is ≳99.99%. Therefore, CDG-2 may be the most GC dominated galaxy and potentially one of the most dark matter dominated galaxies ever discovered. 

We present MArk-dependently THinned POint Process (Mathpop), a novel method to infer the globular cluster (GC) counts in ultra-diffuse galaxies (UDGs) and low-surface brightness galaxies (LSBGs). Many known UDGs have a surprisingly high ratio of GC number to surface brightness. However, standard methods to infer GC counts in UDGs face various challenges, such as photometric measurement uncertainties, GC membership uncertainties, and assumptions about the GC luminosity functions (GCLFs). Mathpop tackles these challenges using the mark-dependent thinned point process, enabling joint inference of the spatial and magnitude distributions of GCs. In doing so, Mathpop allows us to infer and quantify the uncertainties in both GC counts and GCLFs with minimal assumptions. As a precursor to Mathpop, we also address the data uncertainties coming from the selection process of GC candidates: we obtain probabilistic GC candidates instead of the traditional binary classification based on the color–magnitude diagram. We apply Mathpop to 40 LSBGs in the Perseus cluster using GC catalogs from a Hubble Space Telescope imaging program. We then compare our results to those from an independent study using the standard method. We further calibrate and validate our approach through extensive simulations. Our approach reveals two LSBGs having GCLF turnover points much brighter than the canonical value with Bayes’ factor being ∼4.5 and ∼2.5, respectively. An additional crude maximum-likelihood estimation and simulation study show that their GCLF TO points are approximately 0.9 mag and 1.1 mag brighter than the canonical value, with p-values of ∼10^−8 and ∼10^−5, respectively.