Widefield Arecibo Virgo Extragalactic Survey: II. Characterizing the HI properties and environment of the WAVES South region

The paper presents the results from the WAVES South region of the Virgo cluster, a 20 deg2 survey conducted with the Arecibo telescope. The study catalogs 56 Hi sources detected at a median rms noise of 0.8 mJy, including 50 galaxies, two gas clouds (one optically dark), and the ALFALFA Virgo 7 complex. The authors compare these findings with the previously studied VC1 region, finding a significantly lower detection fraction in WAVES South, which they interpret as evidence that WAVES South is a more dynamically relaxed and evolved environment. A stacking analysis of radio spectra across WAVES South, VC1, and VC2, reaching a noise level of 0.080 mJy with up to 157 stacked objects, yielded no new Hi detections. The presence of residual Hi in a small subset of early-type galaxies supports a model of dwarf irregular to dwarf elliptical transformation via environmental stripping, and the authors note a possible evolutionary link between optically dark clouds and recently discovered blue blobs.

Key findings and conclusions from the paper include:

• Detection statistics: We detected 56 Hi sources with a median root mean square (rms) noise of 0.8 mJy, including 50 galaxies, two gas clouds (one being optically dark), and the ALFALFA Virgo 7 complex. The sample includes 47 individual galaxies (3 early-type and 44 late-type), 3 close pairs, and the AV7 complex counted as four sources.

• Comparison with VC1: Our results reveal a significantly lower detection fraction in WAVES South compared to the VC1 region. The Hi detection fraction for VCC galaxies is 14% in WAVES South versus 22% in VC1, and this disparity persists even when restricting to the 17 Mpc main cluster body (11% vs 17%).

• Velocity distributions: "In the VC1 region, Hi-detected and non-detected galaxies exhibit clearly distinct velocity profiles. In contrast, both populations in WAVES South show remarkably similar ranges and shapes, suggesting that the gas-rich and gas-poor systems have had sufficient time to reach a state of dynamical mixing."

• Dark clouds: While VC1 hosts eight isolated dark clouds, WAVES South contains only one truly dark candidate, WCS 54. Crucially, WCS 54 is not isolated but is connected to the galaxy WCS 40 (VCC 952) via an Hi bridge. This suggests a recent ram-pressure stripping formation mechanism.

• Stacking results: Stacking showed no new Hi detection at a 0.080 mJy rms with a maximum of 157 stacked objects from WAVES South, VC1, and VC2. The most significant constraint was a 3σ Hi upper limit of 1.26×106 M⊙. The paper states: "The absence of an Hi signal, even in our deepest aggregate stacks, suggests that once a galaxy’s neutral gas content is reduced below the detection limits of surveys such as ALFALFA or AGES, it is rapidly and effectively removed."

• Early-type galaxies with Hi: we were able to detect two ETGs in Hi within WAVES South: the dE WCS 47 (VCC 21) and the dwarf spheroidal WCS 51 (VCC 651). Both exhibit Hi-to-stellar mass fractions significantly below the median for our Hi-detected sample, consistent with the late stages of a stripping-induced transformation and supporting the Boselli et al. (2008) model.

• Blue blobs: "The blue blob population is remarkably prominent in this part of the cluster. Combined, the WAVES South and VC1 regions contain almost half of the D25 catalog, with both regions hosting exactly five BBs detected in Hi and four that are undetected. The paper notes a potential evolutionary connection between 'dark' clouds and BBs, where the clouds represent a precursor stage to the star-forming blue blob phase."

• Hi deficiency: The distributions are broadly similar but offset by roughly 0.2 dex, with median deficiency of 0.48 for WAVES South and 0.70 in VC1, with statistical tests indicating the populations differ at a 99.3% confidence level. There is a steadily declining trend in Hi deficiency with increasing distance from the cluster center (the X-ray filament between M87 and M49).

• Gas fraction: "The three Hi-detected ETGs exhibit comparable Hi masses (∼ 3 × 107 M⊙), while their stellar masses roughly span an order of magnitude (∼ 107 −108 M⊙). As a result, their gas fractions are systematically lower than those of the LTG population. This is in contrast to VC1, where the Hi-detected dEs had typically higher gas fractions."

• Non-detected late-type galaxies: The authors identified two groups of true LTGs lacking Hi, with the SDSS/WISEA group showing "similarity... consistent with the scenario proposed in Kleiner et al. (2023), namely, that these are the result of very recent RPS, with the galaxies not having yet evolved significantly in color and structure since their gas-loss phase."

• Missing gas: approximately 3 × 1010 M⊙ is 'missing' from the cluster galaxies in WAVES South, and even accounting for all extragalactic gas detected (including BBs, WCS 54, WCS 11, and AV7) would account for less than 7% of this total deficit.

• Overall conclusion: "The lower Hi detection fraction suggests that WAVES South is a more dynamically relaxed and evolved environment than the VC1 region. The presence of residual Hi in a small subset of early-type galaxies supports a model of dwarf irregular to dwarf elliptical transformation via environmental stripping. Finally, we note a possible evolutionary link between optically dark clouds and recently discovered 'blue blobs.'"

Based on this paper, here are specific improvements an AI system can make:

1. Predict galaxy gas-loss stage from multi-wavelength data. The AI can classify galaxies into evolutionary stages (pre-stripping, actively stripping, post-stripping) by combining Hi detection status, Hi-to-stellar mass ratio, color, and morphology, using the finding that gas-poor and gas-rich populations in dynamically relaxed clusters have similar velocity distributions.

2. Distinguish between ram-pressure stripping and tidal interactions as gas-removal mechanisms. The AI can use the presence and connectivity of Hi bridges (e.g., WCS 54 connected to WCS 40) versus isolated dark clouds to infer the dominant environmental process for a given galaxy or cloud system.

3. Estimate missing gas mass in cluster environments. The AI can integrate Hi deficiency measurements, stacking upper limits (e.g., 1.26×106 M⊙ at 3σ), and known extragalactic gas reservoirs to predict the total gas budget of a cluster, flagging systems where >90% of expected gas is unaccounted for.

4. Identify candidate blue blob precursors from optically dark Hi clouds. The AI can search for Hi clouds with no optical counterpart, no stellar component, and proximity to galaxies (like WCS 54) to predict which are likely to evolve into star-forming blue blobs, based on the proposed precursor-stage link.

5. Automate stacking analysis for deep Hi searches. The AI can optimize stacking parameters (e.g., number of objects, velocity alignment, noise weighting) to reach rms levels like 0.080 mJy, and automatically report non-detections as upper limits, using the paper's finding that no signal appears even with 157 stacked objects.

6. Classify early-type galaxies (ETGs) by their transformation stage. The AI can use Hi mass (3×107 M⊙) and stellar mass (spanning 107–108 M⊙) to identify ETGs in late-stage stripping (low gas fraction) versus those with anomalously high gas fractions (like in VC1), enabling a two-parameter evolutionary classification.

7. Predict Hi detection probability in cluster sub-regions. The AI can use the observed detection fraction disparity (14% in WAVES South vs 22% in VC1) and the distance-dependent Hi deficiency trend to build a spatial model that predicts where Hi is likely to be found in a cluster, improving survey target selection.

8. Detect true late-type galaxies lacking Hi. The AI can cross-match SDSS/WISEA data with Hi non-detections to identify LTGs that have recently undergone ram-pressure stripping but have not yet changed color or structure, using the Kleiner et al. (2023) scenario as a template.

9. Quantify dynamical relaxation from velocity distributions. The AI can compute the overlap between velocity distributions of Hi-detected and non-detected galaxies (e.g., via Kolmogorov-Smirnov tests) to automatically classify a cluster region as relaxed/evolved (similar distributions) versus active (distinct distributions), as demonstrated by WAVES South vs VC1.

10. Generate synthetic Hi maps for cluster simulations. The AI can use the observed source counts (56 sources over 20 deg2), median rms (0.8 mJy), and detection fractions to calibrate mock observations, enabling better forecasting for future surveys (e.g., SKA, FAST) in similar environments.