University of Derby

Gravitational-wave population studies must repeatedly ask whether each of millions of simulated binary black holes would be detected, and each answer conventionally requires generating a waveform and computing a noise-weighted inner product. asmaNet maps 11 binary parameters directly to the phase-averaged optimal signal-to-noise ratio for a single detector, beating MLP, 1D-ResNet and Transformer-encoder baselines at every dataset scale from 100k to 1M samples under a matched training protocol with only about 732k parameters. On the 1M-sample held-out test set it reaches 99.29% accuracy after rank-preserving monotonic calibration, sustaining roughly 120,000 SNR evaluations per second on one GPU.

Gravitational-wave population studies must repeatedly ask whether each of millions of simulated binary black holes would be detected, and each answer conventionally requires generating a waveform and computing a noise-weighted inner product. asmaNet maps 11 binary parameters directly to the phase-averaged optimal signal-to-noise ratio for a single detector, beating MLP, 1D-ResNet and Transformer-encoder baselines at every dataset scale from 100k to 1M samples under a matched training protocol with only about 732k parameters. On the 1M-sample held-out test set it reaches 99.29% accuracy after rank-preserving monotonic calibration, sustaining roughly 120,000 SNR evaluations per second on one GPU.