Yongqi Guo | Searcharxiv

arXiv2026

The ALMA-QUARKS Survey: Properties of Hot Molecular Fragments in the Massive Protocluster IRAS 17233-3606

To investigate the physical mechanisms of fragmentation within the hot molecular core of the massive protocluster IRAS 17233-3606 (G351.78-0.54), we carried out a detailed analysis of continuum and lines, using the ALMA Band 3 data from the ATOMS survey and Band 6 data from the QUARKS survey. The low-resolution 3 mm data reveal a massive hot core MM1 with a mass of ~81.3 Msun, and a prominent ultracompact (UC) HII region MM2, while the high-resolution data resolve MM1 into 11 hot molecular fragments (HMFs). These HMFs exhibit hot (Trot = 100-310 K) CH3CN and CH3OH emission and high column densities (NH2 > 10^23 cm^-2), indicating their potential to form massive stars. Based on outflows, masers, HII regions, and f[CH3CN/CH3O] abundance ratios, the evolutionary sequences of the 11 HMFs are categorized as phases I to IV. The mean minimum-spanning tree (MST) separation (~1.8 x 10^3 au) of the HMFs is nearly half of the thermal Jeans length (~3.3 x 10^3 au). Together with the Q parameter Q = 0.77 and virial parameter alpha_vir = 0.84 of MM1, these results suggest an evolutionary scenario in which fragmentation is initially driven by thermal instability, followed by global gravitational contraction and growth through active accretion. Meanwhile, feedback from the B2-type zero-age main-sequence (ZAMS) star and the UC HII region significantly influence the morphology and chemical properties of MM1 and MM2. This heterogeneity highlights the role of diverse physical processes taking place in high-mass protoclusters.

astro-ph.GA↗

arXiv2022

DC-SPP-YOLO: Dense Connection and Spatial Pyramid Pooling Based YOLO for Object Detection

Although the YOLOv2 method is extremely fast on object detection, its detection accuracy is restricted due to the low performance of its backbone network and the underutilization of multi-scale region features. Therefore, a dense connection (DC) and spatial pyramid pooling (SPP) based YOLO (DC-SPP-YOLO) method for ameliorating the object detection accuracy of YOLOv2 is proposed in this paper. Specifically, the dense connection of convolution layers is employed in the backbone network of YOLOv2 to strengthen the feature extraction and alleviate the vanishing-gradient problem. Moreover, an improved spatial pyramid pooling is introduced to pool and concatenate the multi-scale region features, so that the network can learn the object features more comprehensively. The DC-SPP-YOLO model is established and trained based on a new loss function composed of MSE (mean square error) loss and cross-entropy loss. The experimental results indicated that the mAP (mean Average Precision) of DC-SPP-YOLO is higher than that of YOLOv2 on the PASCAL VOC datasets and the UA-DETRAC datasets. The effectiveness of DC-SPP-YOLO method proposed is demonstrated.

cs.CV↗