A5010747089- Pulsars and Gravitational Waves Research
- Gamma-ray bursts and supernovae
- Astrophysics and Cosmic Phenomena
- Complex Network Analysis Techniques
- Caching and Content Delivery
- Advanced Graph Neural Networks
- Geophysics and Gravity Measurements
Beihang University
2024
National Space Science Center
2023
Chinese Academy of Sciences
2023
Abstract The Gravitational Wave High-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is a pair of microsatellites (i.e., GECAM-A and GECAM-B) dedicated to monitoring gamma-ray transients including the high-energy electromagnetic counterparts gravitational waves, such as bursts, soft repeaters, solar flares, terrestrial flashes. Since launch in 2020 December, GECAM-B has detected hundreds astronomical events. For these localization key for burst identification classification well...
<p indent="0mm">The Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM) is proposed to explore the significant opportunity of gravitational multi-messenger astronomy by monitoring electromagnetic counterpart in all-sky. Based on satellite science and observation requirements, we propose design philosophy schemes GECAM system, including system configuration main technical parameters, requirement analysis, orbit, attitude, operation mode design. Further,...
Abstract Fast and reliable localization of high-energy transients is crucial for characterizing the burst properties guiding follow-up observations. Localization based on relative counts different detectors has been widely used all-sky gamma-ray monitors. There are two major methods this count distribution localization: χ 2 minimization method Bayesian method. Here we propose a modified that could take advantage both accuracy simplicity With comprehensive simulations, find our with Poisson...
Recent years have witnessed the abundant emergence of heterogeneous graph neural networks (HGNNs) for link prediction. In graphs, different meta-paths connected to nodes reflect aspects nodes' properties. Existing work fuses multi-aspect properties each node into a single vector representation, which makes them fail capture fine-grained associations between multiple To this end, we propose network with Multi-Aspect Node Association awareness, namely MANA. MANA leverages key among achieve...