Search for: All records

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. 

Abstract This paper presents StarV, a new tool for verifying deep neural networks (DNNs) and learning-enabled Cyber-Physical Systems (Le-CPS) using the well-known star reachability. Distinguished from existing star-based verification tools such as NNV and NNENUM and others, StarV not only offers qualitative verification techniques using Star and ImageStar reachability analysis but is also the first tool to propose using ProbStar reachability for quantitative verification of DNNs with piecewise linear activation functions and Le-CPS. Notably, it introduces a novel ProbStar Temporal Logic formalism and associated algorithms, enabling the quantitative verification of DNNs and Le-CPS’s temporal behaviors. Additionally, StarV presents a novel SparseImageStar set representation and associated reachability algorithm that allows users to verify deep convolutional neural networks and semantic segmentation networks with more memory efficiency. StarV is evaluated in comparison with state-of-the-art in many challenging benchmarks. The experiments show that StarV outperforms existing tools in many aspects, such as timing performance, scalability, and memory consumption.