<table><caption>Table 7
(Continued)</caption><thead><tr><th>#</th><th>ID [NGVSJ]</th><th>NGVS</th><th>VCC</th><th>α(2000)<br>(deg)</th><th>δ(2000)<br>(deg)</th><th>g'(mag)</th><th>Type</th><th>Notes</th></tr></thead><tbody><tr><td>61</td><td>12:42:26.51+11:44:25.2</td><td>3321</td><td>1921</td><td>190.61</td><td>11.74</td><td>15.39</td><td>ES(n|di|pm:-)I(c:)</td><td>Nucleated ES galaxy. Galaxy appears disklike in its center, but isophotes become very boxy (almost peanut- or bow-tie shaped) at large radius. Located 489" to the northeast of NGVS 3296 (VCC1903, M59), although its velocity is larger (1139 km s<sup>-1</sup> vs. 467 km s<sup>-1</sup> for M59). May be associated with VCC1951, which is located 9.1 to the southeast and has a similar velocity (1138 km s<sup>-1</sup> for VCC1921 and 1106 km s<sup>-1</sup> for VCC1951). Possible postmerger remnant. Classified as a "certain" member. Cross ID: VCC1921</td></tr><tr><td>62</td><td>12:44:45.45+12:21:03.4</td><td>3456</td><td>2006</td><td>191.19</td><td>12.35</td><td>13.60</td><td>ES(sh:|du|pm:-)I(g|d)</td><td>ES galaxy with boxy structure, ongoing star formation, dust, and possible weak shells along the major axis. Possible postmerger remnant. Classified as a "certain" member. Cross ID: VCC2006</td></tr><tr><td>63</td><td>12:45:50.97+15:29:29.2</td><td>3519</td><td>...</td><td>191.46</td><td>15.49</td><td>19.68</td><td>E(n|pm:-)Q</td><td>Compact, elongated, very LSB ETG with bright nucleus and outward flaring. Possible bow-tie morphology. Located 52" to the northwest of NGVS 3521, although the velocity of either galaxy is unknown. Possible postmerger remnant. Classified as a "probable" member</td></tr><tr><td>64</td><td>12:46:15.32+10:12:19.8</td><td>3531</td><td>2037</td><td>191.56</td><td>10.21</td><td>15.30</td><td>EI(du|di:|sh|pm:-)I(c)</td><td>Elongated EI galaxy with active star formation in its core. Unusual series of shells or lobes along its major axis at both ends. 3528 (VCC2037) is located 190" to the southwest and has similar velocity (1139 km s<sup>-1</sup> for VCC2037 and 1511 kms for VCC2034). Possible postmerger remnant. Another galaxy, NGVS 3530 is located 154" to the north. Classified as a "certain" member. Cross ID: VCC2037</td></tr><tr><td>65</td><td>12:48:15.11+10:58:60.0</td><td>3597</td><td>2066</td><td>192.06</td><td>10.98</td><td>11.66</td><td>ES(n:|di|sh:|st:|du|pm:-)I(c)</td><td>Possibly nucleated ES galaxy with irregular star-forming component in a diffuse, highly structured ETG-like component. Network of shells and ripples. The galaxy is likely interacting with the smaller irregular galaxy NGVS 3589 (VCC2062), which is located 216" to the west and has a similar velocity (1160 vs. 1140 km s<sup>-1</sup>). Possible postmerger remnant. Classified as a "certain" member. Cross ID: VCC2066</td></tr><tr><td>66</td><td>12:49:39.54+12:37:31.5</td><td>3634</td><td>...</td><td>192.41</td><td>12.63</td><td>14.97</td><td>ES(n|di|sp:|du:|sh:|pm:-)I(d:)</td><td>Nucleated, likely ES galaxy seen edge-on, with possible low-level star formation and shells. Possible postmerger remnant. Classified as a "certain" member</td></tr></tbody></table>
<figure><img src="image_18.png" /><figcaption>**Figure 15.** A mosaic of 66 candidate merger or postmerger galaxies from the NGVS. Each panel measures 3' × 3' with north at the top and east to the left.</figcaption></figure>

structure: concentration measures the fraction of light contained within a central aperture, asymmetry captures the difference between a galaxy and its image rotated by 180° and smoothness assesses the presence of small-scale substructures by comparing the original image to a smoothed version. Together, these metrics provide insight into the morphological complexity of a galaxy. This system has been successfully applied to both simulated and observed galaxy populations (e.g., K. M. Hambleton et al. 2011; A. Whitney et al. 2021; S. Wilkinson et al. 2022, 2024; L. Ferreira et al. 2023) to explore properties such as star formation and degree of morphological transformation from galaxy interactions and mergers.

The CAS system can, as we show below, be unreliable without some restriction on our sample. These statistics were developed specifically for use on bright, high-mass galaxies observed with space-based telescopes. Since the majority of our sample are faint, diffuse galaxies, the A and S statistics in particular will suffer from the inclusion of near-zero pixel values (E. Sazonova et al. 2024). Very smooth and diffuse galaxies are smoothed further, due to their faint nature, in the case of S, which can erase the little morphological information available in the images. These factors will limit the utility of CAS measurements for our sample, restricting their application to only high- and intermediate-luminosity galaxies. The concentration parameter (C) is more robust to the effects of low-S/N imaging and can be measured reliably for a larger number of objects.

The G-M$_{20}$ system (J. M. Lotz et al. 2004) uses the Gini coefficient (G) to measure the concentration of light in an image, irrespective of where the light is concentrated. This makes it particularly sensitive to off-center knots of star formation, providing discrimination between centrally

concentrated ETGs and star-forming, late-type galaxies or morphologically disturbed systems. By contrast, the M$_{20}$ statistic measures the brightest 20% of galaxy flux normalized by the total second moment of light of the galaxy. It provides spatial information on the separation between the brightest regions of a galaxy, making it sensitive to disturbed morphologies and merger signatures.

With these considerations in mind, we used the *statmorph* code (V. Rodriguez-Gomez et al. 2019) to measure nonparametric morphologies for Virgo galaxies based on g'-band images from the NGVS. For the reasons described above, we restricted our analysis to galaxies with reliable measurements following the recommended procedures from V. Rodriguez-Gomez et al. (2019). Galaxies with low-quality or unreliable *statmorph* measurements were rejected, as denoted by an output *Flag* ≥ 2. We also rejected galaxies with images having low mean S/N per pixel, ⟨S/N⟩ ≤ 2.5 (J. M. Lotz et al. 2006), since *statmorph* fits are known to be unreliable in this noise regime. We applied one final cut by removing all galaxies with M$_{g}$ ≥ −13, as galaxies fainter than this often had suspect *statmorph* output diagnostic plots and measured nonparametric morphological statistics. Applying these cuts reduced the size of the NGVS sample to 762 galaxies with trustworthy *statmorph* morphologies (i.e., just the brightest ~ one-fifth of the catalog).

We examined a variety of CAS diagnostic plots using the available parameters (C, A, S, M$_{20}$, and G) in order to find the most instructive combinations. Figure 16 shows some general trends exhibited by our cleaned sample of galaxies. Results are shown after color coding the galaxies by both structural code (left panels) and star formation code (right panels). In each case, the first panel shows the distribution of galaxies in the M$_{g}$-(u − i) CMD. The remaining three panels show different
<figure><img src="image_19.png" /><figcaption>**Figure 16.** CMD for NGVS galaxies, along with several scatter plots using nonparametric statistics. Galaxies are labeled by their structural code (left) and star formation code (right) for the sample of 762 galaxies having *statmorph* measurements with $M_g \lesssim -13$ as discussed in the text.</figcaption></figure>

combinations of CASGM$_{20}$ parameters: (1) C versus $M_{20}$; (2) G versus $M_{20}$; and (3) C versus G.

Figure 16 suggests that elevated rates of star formation are correlated with higher $M_{20}$ at fixed C and G; this is more obvious in the right-hand panels, which are color coded by star formation code. This trend is expected, as highly star-forming galaxies exhibit bluer colors. They also tend to appear more concentrated at a fixed $M_{20}$ and have higher G values at the same $M_{20}$. The panels on the right side of the figure show the general trend, but with galaxies color coded by structural code. Outside of the expected separation seen in color–magnitude space between early- and late-type morphologies, spanned by transition-type structure codes, the distinctions between populations are more subtle in nonparametric morphology space. E-type, quiescent galaxies tend to show lower concentrations at constant $M_{20}$ values compared to later-type objects with A or I star formation codes. ES galaxies tend to define the tail of the main galaxy population in the C–$M_{20}$ plane, with high C values and more negative $M_{20}$ values.

### 5.2.2. CASGM$_{20}$ Measurements for Compact Galaxies

To illustrate the connections between our visual morphologies, nonparametric CASGM$_{20}$ measurements, and NGVS catalog parameters, we consider the population of compact galaxies discussed in Section 5.1.1. These objects are attractive targets for such a comparison because they are bright enough to have reliable *statmorph* measurements and may be undergoing morphological changes within the cluster environment.

Figure 17 shows the same scatter plots as Figure 16, but now with the sample of compact galaxies from Section 5.1.1 highlighted as large squares. As before, the structural and star formation codes for individual galaxies (compact objects

included) are given at the top of the left and right panels, respectively.

The compact galaxies in Figure 17 appear to show two main components in the CMDs—a blue, star-forming population and a redder, quiescent population. This separation is most easily seen in the right panels, where galaxies are labeled by their star formation code. In the right panels, where galaxies are labeled by their structural code, the picture is less clear, but we still see that galaxies having higher levels of morphological complexity are generally found to have higher values of C, G, and $M_{20}$, indicative of ongoing star formation.

Within the CASGM$_{20}$ scatter plots, most of the compact galaxies fall inside the main galaxy distributions in the $M_{20}$–C, $M_{20}$–G, and C–G planes. There is, however, a slight separation within these clouds according to both structural code and star formation code: i.e., earlier morphologies and quiescent types have, on average, slightly lower values of $M_{20}$ at constant C. A small number of extreme outliers fall well beyond the main cloud of points in each of these relations. These outliers are a mixture of structural codes and star formation codes but share the common feature of having a small number of bright pixels in their inner regions, either the result of a steeply rising brightness profile, in the case of M32-like systems, or the presence of compact star-forming regions, in the case of BCD-like systems.

### 5.3. Application to Machine Learning Tools

Ongoing and imminent imaging surveys—such as the Ultra-violet Near Infrared Optical Northern Survey (K. Chambers & Unions 2023; S. Gwyn et al. 2025), Euclid (R. Laureijs et al. 2011), and LSST (Ž. Ivezić et al. 2019)—will deliver huge datasets containing billions of galaxies. Such data volumes far exceed the capacity for human classification, so machine learning and deep learning techniques will be essential for efficiently
<figure><img src="image_20.png" /><figcaption>**Figure 17.** Same as Figure 16 except with compact galaxies from Section 5.1.1 denoted by large squares. We note that compact galaxies bifurcate in the CMD as expected and that these galaxies are generally categorized as being more morphologically complex with higher values of C, G, and $M_{20}$.</figcaption></figure>

analyzing these data (e.g., O. Lahav et al. 1995; S. Dieleman et al. 2015; H. Domínguez Sánchez et al. 2019; M. Walmsley et al. 2020; T.-Y. Cheng et al. 2021). It is hoped that such tools will be able to efficiently identify and classify galaxies of all morphological types, including objects that have often eluded detection and classification, such as LSB galaxies and/or low-mass dwarfs.

To assess the compatibility of our classification system with past machine learning approaches, we crossmatched our NGVS sample with the ~700,000 galaxies in M. Huertas-Company et al. (2011; HC2011). That early, pioneering study used a support vector machine (SVM) trained on a visually classified subset of SDSS galaxies to assign probabilistic morphological types: E, S0, Sab, and Scd. Each galaxy in their catalog is associated with a probability vector over these four types, reflecting the SVM's learned decision boundaries from the training set.

We identified 462 unambiguous matches (within 1") between the NGVS and HC2011. These galaxies lie exclusively at the bright end of the NGVS sample ($g \lesssim 18$ mag), where SDSS detections are reliable and morphologies are well constrained. Within this matched subset, 57% are classified as E, 26% as ES, and 10% as S, SI, or I in the NGVS morphology scheme.

Among the NGVS sample of E galaxies, 48% are classified as Sab and 42% as Scd by HC2011, while NGVS ES galaxies are classified as Sab and Scd in 43% and 50% of cases, respectively. These results indicate poor agreement between the schemes for early-type systems, with most NGVS ellipticals and compact spheroidals being misclassified as late-type spirals by the HC2011 model. This almost certainly reflects the limited number of compact or LSB galaxies in their training set, which biases the classifier toward later types when applied to diffuse systems. For NGVS spirals and transition-types (e.g., S, SI, I), 76% are classified as Scd and 24% as Sab, more consistent with expectations. These findings highlight the potential of the NGVS catalog as a training set for

future morphological classification models, including learning methods that aim to infer galaxy morphologies directly from imaging data (M. Banerji et al. 2010; H. Domínguez Sánchez et al. 2018; C. Bottrell et al. 2019; R. W. Bickley et al. 2021; M. Walmsley et al. 2022, 2023).

## 6. Summary and Conclusions

The NGVS used the MegaCam instrument on the CFHT to perform deep, multiband (*u* giz) optical imaging of the Virgo Cluster from its central core out to its virial radius. This region —covering an area of ~100 deg² centered on the largest overdensity in the local Universe—contains a vast reservoir of galaxies that span nearly all known morphological types.

As described in L. Ferrarese et al. (2016, 2020), the NGVS images have been carefully searched for Virgo cluster members using a variety of techniques, resulting in a final catalog of 3689 confirmed, probable, and possible cluster members (L. Ferrarese et al. 2025, in preparation). While the catalog includes more than 100 distinct photometric and structural parameters measured for each cluster member, future studies based on this sample and survey would benefit from the addition of morphological information on the sample. Although visual morphologies have some well-known limitations (e.g., they are inherently subjective, to some degree), such classification continues to see widespread use within the community, especially in the selection of targets and subsamples for follow-up studies.

In this paper, we have introduced a customized classification framework for NGVS galaxies. This system builds on previous morphological classification schemes while taking advantage of the depth, angular resolution, and multiwavelength coverage of the NGVS imaging, with the flexibility to describe high- and low-mass galaxies within a single framework. Each member galaxy was assigned two parameters that broadly describe its global structure and current level of star formation activity. In
terms of structure, galaxies are divided into six broad categories, including transitional types: i.e., *E*, *ES*, *S*, *SI*, *EI*, and *I*. The morphology catalog will be made publicly available upon the publication of the full NGVS catalog (L. Ferrarese et al. 2025, in preparation).

Our morphological system is shown to be self-consistent among classifiers, with good internal agreement. Comparisons with previous Virgo surveys, such as the VCC (B. Binggeli et al. 1985) and EVCC (S. Kim et al. 2014), also show a high degree of consistency while revealing additional structural details due to the fainter point-source and surface brightness limits of the CFHT imaging. We confirm a gentle morphology–density relation within Virgo, consistent with prior studies of the cluster, but with an expanded sample.

Examining our morphological classifications, we highlight several galaxy types of interest for future study, including compact galaxies, UDGs, possible examples of ultracompact galaxies (UCGs) forming through the disruption of nucleated dwarfs, and candidate merger and postmerger systems. These objects may potentially serve as testbeds for understanding the physical mechanisms shaping galaxy structure and star formation histories, offering constraints before the era of big-data-driven astronomical surveys makes it nearly impossible to visually classify galaxies and manually verify derived models and outputs. Nonparametric morphological measures, which are reliable for the brightness of ~760 galaxies in our sample, provide insight into the underlying physical processes.

This study bridges classical classification methods with modern, data-driven approaches, providing a new perspective on galaxy formation and evolution in a cluster environment, and spanning roughly seven decades in stellar mass. The next generation of wide-field imaging surveys will push the limits of spatial resolution and depth, and the NGVS may serve as a benchmark for calibrating and interpreting automated classification frameworks. By combining visual and nonparametric techniques, this work lays the foundation for deeper insights into the structural evolution of galaxies, particularly those in dense environments.

## Acknowledgments

The authors thank Hong-Xin Zhang for insightful discussions on the selection of candidate merger and postmerger systems. The authors acknowledge the use of the Canadian Advanced Network for Astronomy Research (CANFAR) Science Platform. Our work used the facilities of the Canadian Astronomy Data Center, operated by the National Research Council of Canada with the support of the Canadian Space Agency, and CANFAR, a consortium that serves the data-intensive storage, access, and processing needs of university groups and centers engaged in astronomy research (S. Gaudet et al. 2010). This research has also made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). S.L. acknowledges the support from the Sejong Science Fellowship Program by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT; No. NRF-2021R1C1C2006790). P. G. acknowledges support from NSF grant AST-2206328. E.T. is thankful for the support from the NSF-AST-2206498 grant. C.L. acknowledges support from the National Natural Science

Foundation of China (NSFC; grant No. 12173025), National Key R&D Program of China (2023YFA1607800, 2023YFA1607804), 111 project (No. B20019), and Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education. M.A.T. and S.T. acknowledge the support of the NSERC, 2023-03298.

*Facility:* MegaCam(CFHT).

*Software:* Astropy (Astropy Collaboration et al. 2013, 2018), Source Extractor (E. Bertin & S. Arnouts 1996), matplotlib (J. D. Hunter 2007), numpy (C. R. Harris et al. 2020), pandas (W. McKinney 2010; J. Reback et al. 2020), photutils (L. Bradley et al. 2024), seaborn (M. Waskom 2021).

## ORCID iDs

Max M. Kurzner https://orcid.org/0000-0001-5208-4697

Patrick Côté https://orcid.org/0000-0003-1184-8114

Laura Ferrarese https://orcid.org/0000-0002-8224-1128

Kaixiang Wang https://orcid.org/0000-0003-4672-8497

Eric W. Peng https://orcid.org/0000-0002-2073-2781

Scott Wilkinson https://orcid.org/0000-0002-3303-4077

Joel C. Roediger https://orcid.org/0000-0002-0363-4266

Chelsea Spengler https://orcid.org/0000-0002-1685-4284

Toby Brown https://orcid.org/0000-0003-1845-0934

Chengze Liu https://orcid.org/0000-0002-4718-3428

Sungsoon Lim https://orcid.org/0000-0002-5049-4390

Rubén Sánchez-Janssen https://orcid.org/0000-0003-4945-0056

Elisa Toloba https://orcid.org/0000-0001-6443-5570

Puragra Guhathakurta https://orcid.org/0000-0001-8867-4234

John P. Blakeslee https://orcid.org/0000-0002-5213-3548

Patrick R. Durrell https://orcid.org/0000-0001-9427-3373

Ariane Lançon https://orcid.org/0000-0002-7214-8296

J. Christopher Mihos https://orcid.org/0000-0002-7089-8616

Matthew A. Taylor https://orcid.org/0000-0003-3009-4928

Tyrone E. Woods https://orcid.org/0000-0003-1428-5775

Solveig Thompson https://orcid.org/0009-0006-7485-7463

Lauren A. MacArthur https://orcid.org/0009-0003-5548-6773

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