pith:FD65APP2
Manifold Learning for Source Separation in Confusion-Limited Gravitational-Wave Data
Adding manifold normalization to an autoencoder anomaly score improves separation of resolvable sources from LISA's galactic confusion background.
arxiv:2511.12845 v4 · 2025-11-17 · physics.gen-ph
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Claims
With this combination, the method achieves an AUC of 0.752, precision 0.81, and recall 0.61, a 35% improvement over the autoencoder alone.
The synthetic LISA data sets used for training and testing, including the modeled confusion background and injected sources, sufficiently resemble the actual data LISA will record so that performance on these simulations predicts performance on flight data.
A CNN autoencoder plus manifold normalization in latent space detects injected sources in synthetic LISA confusion data with AUC 0.752, precision 0.81 and recall 0.61, a 35% gain over autoencoder error alone.
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| First computed | 2026-06-23T01:11:58.862723Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
28fdd03dfaf169ee451c6216396cc87007179e990f97dbac09cacb6e250e1247
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/FD65APP26FU64RI4MILDS3GIOA \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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