Three recent papers involving U²GRC researchers highlight new work in gravitational-wave data analysis and waveform modeling.

Efficient template placement for gravitational-wave searches

Alexandra Wernersson, Jessica Irwin, Melissa Lopez, and Sarah Caudill introduce isobank, a machine-learning approach for placing waveform templates efficiently in the parameter spaces used in gravitational-wave searches. On an eccentric-binary example, the method achieved strong coverage with fewer templates than a leading stochastic-placement approach.

Guiding binary-black-hole searches with neural networks

In new work, Chetan Verma, Amit Reza, Gurudatt Gaur, Dilip Krishnaswamy, and Sarah Caudill study how neural networks can narrow the part of binary-black-hole parameter space that needs to be searched. Their results examine the influence of different parameter representations on this targeted-search strategy.

An improved precessing waveform surrogate

Abhishek Ravishankar, Vijay Varma, Scott E. Field, Keefe Mitman, Nils Deppe, Leo C. Stein, Michael Boyle, and Mark A. Scheel present NRSur7dq4v2, an updated surrogate model for precessing binary-black-hole mergers. The model improves accuracy in the merger and ringdown, expands the modeled waveform content, and accelerates evaluation for applications in gravitational-wave inference.