pith:KV76M27G
Parameter Estimation Horizon of Core-Collapse Supernovae with Current and Next-Generation Gravitational-Wave Detectors
Machine learning on gravitational-wave signals from core-collapse supernovae can constrain core rotation out to more than 100 kpc with next-generation detectors for favorable orientations.
arxiv:2605.04896 v1 · 2026-05-06 · astro-ph.HE
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Claims
For optimal orientations, next-generation detectors can constrain rotation out to distances exceeding 100 kpc.
The machine learning models trained on a finite set of simulated signals from specific progenitors and equations of state will generalize accurately to real gravitational-wave observations without significant biases from unmodeled physics or detector effects.
Machine learning extracts core rotation and signal properties from CCSN gravitational waves, with next-generation detectors constraining rotation beyond 100 kpc for favorable orientations despite some uncertainties.
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| First computed | 2026-06-24T01:15:03.527015Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
557fe66be693b9bce8830021e2c77442d2eb8ccfddb2ff32850f0c8b610e0262
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/KV76M27GSO43Z2EDAAQ6FR3UIL \
| 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: 557fe66be693b9bce8830021e2c77442d2eb8ccfddb2ff32850f0c8b610e0262
Canonical record JSON
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