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pith:2025:FD65APP26FU64RI4MILDS3GIOA
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Manifold Learning for Source Separation in Confusion-Limited Gravitational-Wave Data

Jericho Cain

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

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

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

Aliases

arxiv: 2511.12845 · arxiv_version: 2511.12845v4 · doi: 10.48550/arxiv.2511.12845 · pith_short_12: FD65APP26FU6 · pith_short_16: FD65APP26FU64RI4 · pith_short_8: FD65APP2
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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())"
# expect: 28fdd03dfaf169ee451c6216396cc87007179e990f97dbac09cacb6e250e1247
Canonical record JSON
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    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.gen-ph",
    "submitted_at": "2025-11-17T00:27:42Z",
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