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.
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 …
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.
Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges …
Analysis of LISA-TAIJI network configurations for detecting stochastic gravitational wave background from cosmic strings, finding LISA-TAIJIc offers optimal sensitivity for constraining string tension at G𝜇~10^-17.
Deep learning approach for EMRI detection achieving 94.2% TPR at 1% FPR, demonstrating the potential for efficient signal detection in space-based gravitational wave detectors.
Science-driven multi-stage deep neural network for space-based gravitational wave detection and extraction achieving >99% accuracy and ≥95% signal similarity, with strong generalization and interpretability demonstrated on synthetic LISA data.