Engineering topics
Gong, Hao
Publications and source records attributed to Gong, Hao.
CARMA Large Area Star Formation Survey: project overview with analysis of dense gas structure and kinematics in Barnard 1
We present details of the CARMA Large Area Star Formation Survey (CLASSy), while focusing on observations of Barnard 1. CLASSy is a CARMA Key Project that spectrally imaged N{sub 2}H{sup +}, HCO{sup +}, and HCN (J = 1 → 0 transitions) across over 800 square arcminutes of the Perseus and Serpens Molecular Clouds. The observations have angular resolution near 7'' and spectral resolution near 0.16 km s{sup –1}. We imaged ∼150 square arcminutes of Barnard 1, focusing on the main core, and the B1 Ridge and clumps to its southwest. N{sub 2}H{sup +} shows the strongest emission, with morphology similar to cool dust in the region, while HCO{sup +} and HCN trace several molecular outflows from a collection of protostars in the main core. We identify a range of kinematic complexity, with N{sub 2}H{sup +} velocity dispersions ranging from ∼0.05 to 0.50 km s{sup –1} across the field. Simultaneous continuum mapping at 3 mm reveals six compact object detections, three of which are new detections. A new, non-binary dendrogram algorithm is used to analyze dense gas structures in the N{sub 2}H{sup +} position-position-velocity (PPV) cube. The projected sizes of dendrogram-identified structures range from about 0.01 to 0.34 pc. Size-linewidth relations using those structures show that non-thermal line-of-sight velocity dispersion varies weakly with projected size, while rms variation in the centroid velocity rises steeply with projected size. Comparing these relations, we propose that all dense gas structures in Barnard 1 have comparable depths into the sky, around 0.1-0.2 pc; this suggests that overdense, parsec-scale regions within molecular clouds are better described as flattened structures rather than spherical collections of gas. Science-ready PPV cubes for Barnard 1 molecular emission are available for download.
Interior micro-CT with an offset detector
Purpose: The size of field-of-view (FOV) of a microcomputed tomography (CT) system can be increased by offsetting the detector. The increased FOV is beneficial in many applications. All prior investigations, however, have been focused to the case in which the increased FOV after offset-detector acquisition can cover the transaxial extent of an object fully. Here, the authors studied a new problem where the FOV of a micro-CT system, although increased after offset-detector acquisition, still covers an interior region-of-interest (ROI) within the object. Methods: An interior-ROI-oriented micro-CT scan with an offset detector poses a difficult reconstruction problem, which is caused by both detector offset and projection truncation. Using the projection completion techniques, the authors first extended three previous reconstruction methods from offset-detector micro-CT to offset-detector interior micro-CT. The authors then proposed a novel method which combines two of the extended methods using a frequency split technique. The authors tested the four methods with phantom simulations at 9.4%, 18.8%, 28.2%, and 37.6% detector offset. The authors also applied these methods to physical phantom datasets acquired at the same amounts of detector offset from a customized micro-CT system. Results: When the detector offset was small, all reconstruction methods showed good image quality. At large detector offset, the three extended methods gave either visible shading artifacts or high deviation of pixel value, while the authors’ proposed method demonstrated no visible artifacts and minimal deviation of pixel value in both the numerical simulations and physical experiments. Conclusions: For an interior micro-CT with an offset detector, the three extended reconstruction methods can perform well at a small detector offset but show strong artifacts at a large detector offset. When the detector offset is large, the authors’ proposed reconstruction method can outperform the three extended reconstruction methods by suppressing artifacts and maintaining pixel values.