Add GNPEChirp to training pipeline#356
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Add factor_fiducial_waveform() and HeterodynePhase transform for phase heterodyning, and GNPEChirp transform for chirp mass GNPE conditioning. These enable removing the dominant chirp phase evolution from BNS waveforms, which is critical for neural network training. Based on Max Dax's implementation on bns_add_dingo_pipe_max. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
ApplySVD and WhitenFixedASD now operate on sample["waveform"] instead of flat dicts, matching the interface used by all other transforms. This enables HeterodynePhase (which needs sample["parameters"]) to be part of the compression/decompression chain. Also wires in HeterodynePhase to compression (generate_dataset.py) and decompression (waveform_dataset.py), and wraps data as sample dicts in WaveformGenerator and WaveformDataset before passing to transforms. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Wire chirp mass GNPE and physics-based frequency masking into train_builders.py for BNS training support. GNPEChirp samples a chirp mass proxy, applies heterodyning, and adds context parameters. ApplyChirpFrequencyMasking zeros frequency bins below the 0PN inspiral entry frequency for the given chirp mass proxy, avoiding wasted network capacity on empty bins. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
GNPEChirpintoset_train_transforms()intrain_builders.py, parallel to existinggnpe_time_shiftsblockGNPEChirptoomit_transformsinbuild_svd_for_embedding_networkDepends on PR #355 (heterodyning transforms). Once #355 is merged into
bns, this PR's diff will show only these changes.YAML config format
Test plan
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