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Higher-order gravitational wave modes from quasi-circular, spinning, non-precessing binary black hole mergers encode key information about these systems' nonlinear dynamics. We model these waveforms using transformer architectures, targeting the evolution from late inspiral through ringdown. Our data is derived from the \texttt{NRHybSur3dq8} surrogate model, which includes spherical harmonic modes up to ℓ≤4 (excluding (4,0), (4,±1) and including (5,5) modes). These waveforms span mass ratios q≤8, spin components sz1,2∈[−0.8,0.8], and inclination angles θ∈[0,π]. The model processes input data over the time interval t∈[−5000M,−100M) and generates predictions for the plus and cross polarizations, (h+,h×), over the interval t∈[−100M,130M]. Utilizing 16 NVIDIA A100 GPUs on the Delta supercomputer, we trained the transformer model in 15 hours on over 14 million samples. The model's performance was evaluated on a test dataset of 840,000 samples, achieving mean and median overlap scores of 0.996 and 0.997, respectively, relative to the surrogate-based ground truth signals. We further benchmark the model on numerical relativity waveforms from the SXS catalog, finding that it generalizes well to out-of-distribution systems, capable of reproducing the dynamics of systems with mass ratios up to q=15 and spin magnitudes up to 0.998, with a median overlap of 0.969 across 521 NR waveforms and up to 0.998 in face-on/off configurations. These results demonstrate that transformer-based models can capture the nonlinear dynamics of binary black hole mergers with high accuracy, even outside the surrogate training domain, enabling fast sequence modeling of higher-order wave modes. 

Cosmic Explorer is a next-generation ground-based gravitational-wave observatory that is being designed in the 2020s and is envisioned to begin operations in the 2030s together with the Einstein Telescope in Europe. The Cosmic Explorer concept currently consists of two widely separated L-shaped observatories in the United States, one with 40 km-long arms and the other with 20 km-long arms. This order of magnitude increase in scale with respect to the LIGO-Virgo-KAGRA observatories will, together with technological improvements, deliver an order of magnitude greater astronomical reach, allowing access to gravitational waves from remnants of the first stars and opening a wide discovery aperture to the novel and unknown. In addition to pushing the reach of gravitational-wave astronomy, Cosmic Explorer endeavors to approach the lifecycle of large scientific facilities in a way that prioritizes mutually beneficial relationships with local and Indigenous communities. This article describes the (scientific, cost and access, and social) criteria that will be used to identify and evaluate locations that could potentially host the Cosmic Explorer observatories. 

Abstract The collection of gravitational waves (GWs) that are either too weak or too numerous to be individually resolved is commonly referred to as the gravitational-wave background (GWB). A confident detection and model-driven characterization of such a signal will provide invaluable information about the evolution of the universe and the population of GW sources within it. We present a new, user-friendly, Python-based package for GW data analysis to search for an isotropic GWB in ground-based interferometer data. We employ cross-correlation spectra of GW detector pairs to construct an optimal estimator of the Gaussian and isotropic GWB, and Bayesian parameter estimation to constrain GWB models. The modularity and clarity of the code allow for both a shallow learning curve and flexibility in adjusting the analysis to one’s own needs. We describe the individual modules that make up pygwb , following the traditional steps of stochastic analyses carried out within the LIGO, Virgo, and KAGRA Collaboration. We then describe the built-in pipeline that combines the different modules and validate it with both mock data and real GW data from the O3 Advanced LIGO and Virgo observing run. We successfully recover all mock data injections and reproduce published results.