Revolutionary framework using LLMs to automatically design Large Neighborhood Search operators through synergy-aware co-evolution. Achieves near-optimal solutions on TSP and CVRP benchmarks with strong generalization.
A framework for automated discovery of gravitational-wave detection algorithms, combining LLM guidance with Evolutionary Monte Carlo Tree Search, enabling efficient and creative pipeline discovery.
A review of modern simulation-based inference techniques for gravitational wave data analysis, highlighting methodological advances, practical applications, and future outlook.
Detecting the coalescences of massive black hole binaries (MBHBs) is one of the primary targets for space-based gravitational wave observatories such as LISA, Taiji, and Tianqin. The fast and accurate parameter estimation of merging MBHBs is of great …
One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by …
The direct detection of gravitational waves by LIGO has confirmed general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high …
The mergers of massive black hole binaries could generate rich electromagnetic emissions, which allow us to probe the environments surrounding these massive black holes and gain deeper insights into the high energy astrophysics. However, due to the …
Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. …
Comprehensive review of data analysis challenges in space-based gravitational wave detection (LISA, Taiji, TianQin) and transformative applications of artificial intelligence in addressing these challenges.
Transformer-based deep learning model achieving >10× noise reduction with ~1% phase error and ~7% amplitude error on LIGO data, demonstrating significant IFAR improvement on 75 BBH events. Featured work highlighting large neural networks' potential in GW analysis.