3 citations · 3 across the 5 of their papers we have counts for
5 papers
Adaptive Detection of Fast Moving Celestial Objects Using a Mixture of Experts and Physical-Inspired Neural Network
Peng Jia, Ge Li, Bafeng Cheng +2
Fast moving celestial objects are characterized by velocities across the celestial sphere that significantly differ from the motions of background stars. In observational images, t…
Image Pre-Processing Framework for Time-Domain Astronomy in the Artificial Intelligence Era
Liang Cao, Peng Jia, Jiaxin Li +3
The rapid advancement of image analysis methods in time-domain astronomy, particularly those leveraging AI algorithms, has highlighted efficient image pre-processing as a critical…
First Discovery and Confirmation of PN Candidates Found from AI and Deep Learning Techniques Applied to VPHAS+ Survey Data
Yushan Li, Quentin Parker, Peng Jia
Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H surveys of the Galactic Plane for elusive planetary nebulae (PNe) whic…
A Data-Driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes
Peng Jia, Chao Lv, Yushan Li +3
In recent years, there has been a gradual increase in the performance of Complementary Metal Oxide Semiconductor (CMOS) cameras. These cameras have gained popularity as a viable al…
AI Techniques for Uncovering Resolved Planetary Nebula Candidates from Wide-field VPHAS+ Survey Data
Ruiqi Sun, Yushan Li, Quentin Parker +4
AI and deep learning techniques are beginning to play an increasing role in astronomy as a necessary tool to deal with the data avalanche. Here we describe an application for findi…