adsabs/AstroCLIMB huggingface dataset:
Paper: Adaptive Detection of Fast-moving Celestial Objects Using a Mixture-of-experts and Physical-inspired Neural Network
Authors: Peng Jia, Ge Li, Bafeng Cheng, Yushan Li, Rongyu Sun
Figure caption: Three simulated images represent three different observation modes. These images are simulated using Skymaker (E. Bertin 2009). (a) Sidereal tracking mode: this image is generated with a simulated telescope that has a 1 m diameter and a 1° field of view, operating in sidereal tracking mode with an exposure time of 0.5 s. (b) Object tracking mode: this image is created using a simulated telescope with a 60 cm diameter and a 0﹩\mathop{.}\limits^{\unicode{x000b0}}﹩6 field of view, set to object tracking mode with an exposure time of 0.4 s. (c) Intersection mode: this image is produced with a simulated telescope with a 30 cm diameter and a 0﹩\mathop{.}\limits^{\unicode{x000b0}}﹩6 field of view, employing intersection mode with an exposure time of 0.1 s.
Paper: Adaptive Detection of Fast-moving Celestial Objects Using a Mixture-of-experts and Physical-inspired Neural Network
Authors: Peng Jia, Ge Li, Bafeng Cheng, Yushan Li, Rongyu Sun
Figure caption: The structure of the MoE discussed in this paper. Two expert neural networks are used for FMCO detection, while one gating network is used to control these neural networks to output final detection results. Both of these experts are PiNNs. The outputs of E1 and E2 are combined using the weights derived from the gating network to obtain our model.
Paper: Adaptive Detection of Fast-moving Celestial Objects Using a Mixture-of-experts and Physical-inspired Neural Network
Authors: Peng Jia, Ge Li, Bafeng Cheng, Yushan Li, Rongyu Sun
Figure caption: Panel (a) shows the data in sidereal tracking mode, while panel (b) shows the PSF convolved with motion vectors under the same sidereal tracking mode.
Paper: Adaptive Detection of Fast-moving Celestial Objects Using a Mixture-of-experts and Physical-inspired Neural Network
Authors: Peng Jia, Ge Li, Bafeng Cheng, Yushan Li, Rongyu Sun
Figure caption: The schematic draw of the structure of the PiNN proposed in this paper.