Search for: All records

Characterizing the structural properties of galaxies in high-redshift protoclusters is key to our understanding of the environmental effects on galaxy evolution in the early stages of galaxy and structure formation. In this study, we assess the structural properties of 85 and 87 Hα emission-line candidates (HAEs) in the densest regions of two massive protoclusters, BOSS1244 and BOSS1542, respectively, using the Hubble Space Telescope (HST) H-band imaging data. Our results show a true pair fraction of 22 ± 5 (33 ± 6) per cent in BOSS1244 (BOSS1542), which yields a merger rate of 0.41 ± 0.09 (0.52 ± 0.04) Gyr−1 for massive HAEs with log (M*/M⊙) ≥ 10.3. This rate is 1.8 (2.8) times higher than that of the general fields at the same epoch. Our sample of HAEs exhibits half-light radii and Sérsic indices that cover a broader range than field star-forming galaxies. Additionally, about 15 per cent of the HAEs are as compact as the most massive (log (M*/M⊙) ≳ 11) spheroid-dominated population. These results suggest that the high galaxy density and cold dynamical state (i.e. velocity dispersion of <400 km s−1) are key factors that drive galaxy mergers and promote structural evolution in the two protoclusters. Our findings also indicate that both the local environment (on group scales) and the global environment play essential roles in shaping galaxy morphologies in protoclusters. This is evident in the systematic differences observed in the structural properties of galaxies between BOSS1244 and BOSS1542.

 

Recent years have witnessed the superior performance of heterogeneous graph neural networks (HGNNs) in dealing with heterogeneous information networks (HINs). Nonetheless, the success of HGNNs often depends on the availability of sufficient labeled training data, which can be very expensive to obtain in real scenarios. Active learning provides an effective solution to tackle the data scarcity challenge. For the vast majority of the existing work regarding active learning on graphs, they mainly focus on homogeneous graphs, and thus fall in short or even become inapplicable on HINs. In this paper, we study the active learning problem with HGNNs and propose a novel meta-reinforced active learning framework MetRA. Previous reinforced active learning algorithms train the policy network on labeled source graphs and directly transfer the policy to the target graph without any adaptation. To better exploit the information from the target graph in the adaptation phase, we propose a novel policy transfer algorithm based on meta-Q-learning termed per-step MQL. Empirical evaluations on HINs demonstrate the effectiveness of our proposed framework. The improvement over the best baseline is up to 7% in Micro-F1. 

Both the computational costs and the accuracy of the invariant-imbedding T-matrix method escalate with increasing the truncation number N at which the expansions of the electromagnetic fields in terms of vector spherical harmonics are truncated. Thus, it becomes important in calculation of the single-scattering optical properties to choose N just large enough to satisfy an appropriate convergence criterion; this N we call the optimal truncation number. We present a new convergence criterion that is based on the scattering phase function rather than on the scattering cross section. For a selection of homogeneous particles that have been used in previous single-scattering studies, we consider how the optimal N may be related to the size parameter, the index of refraction, and particle shape. We investigate a functional form for this relation that generalizes previous formulae involving only size parameter, a form that shows some success in summarizing our computational results. Our results indicate clearly the sensitivity of optimal truncation number to the index of refraction, as well as the difficulty of cleanly separating this dependence from the dependence on particle shape. 

ABSTRACT Submillimetre galaxies represent a rapid growth phase of both star formation and massive galaxies. Mapping SMGs in galaxy protoclusters provides key insights into where and how these extreme starbursts take place in connections with the assembly of the large-scale structure in the early Universe. We search for SMGs at 850 $\rm{\mu m}$ using JCMT/SCUBA-2 in two massive protoclusters at z = 2.24, BOSS1244 and BOSS1542, and detect 43 and 54 sources with S850 > 4 mJy at the 4σ level within an effective area of 264 arcmin2, respectively. We construct the intrinsic number counts and find that the abundance of SMGs is 2.0 ± 0.3 and 2.1 ± 0.2 times that of the general fields, confirming that BOSS1244 and BOSS1542 contain a higher fraction of dusty galaxies with strongly enhanced star formation. The volume densities of the SMGs are estimated to be ∼15–30 times the average, significantly higher than the overdensity factor (∼6) traced by H α emission-line galaxies (HAEs). More importantly, we discover a prominent offset between the spatial distributions of the two populations in these two protoclusters – SMGs are mostly located around the high-density regions of HAEs, and few are seen inside these regions. This finding may have revealed for the first time the occurrence of violent star formation enhancement in the outskirts of the HAE density peaks, likely driven by the boosting of gas supplies and/or starburst triggering events. Meanwhile, the lack of SMGs inside the most overdense regions at z ∼ 2 implies a transition to the environment disfavouring extreme starbursts.