pith:Y3KTKMRX
Latent-Space Gaussian Processes for Dark-Energy Reconstruction from Observational \(H(z)\) Data
Placing a Gaussian process prior directly on dark-energy density f(z) yields viable reconstructions from Hubble data consistent with Lambda CDM.
arxiv:2605.13427 v1 · 2026-05-13 · astro-ph.CO
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Claims
The latent-f approach is therefore a viable alternative to latent-H, while current constraints are limited mainly by sparse high-redshift OHD and dependence on external priors.
That the chosen Gaussian process kernel and external priors do not systematically bias the reconstruction of f(z) and Om(z) in the high-redshift regime where data coverage is poorest.
Latent-f and latent-H Gaussian process reconstructions from OHD data both yield f(z), w(z), and Om(z) consistent with Lambda-CDM, with no strong predictive preference and small prior-dependent residuals mainly at high redshift.
References
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| First computed | 2026-05-18T02:44:47.247756Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
c6d53532370e829543b24bb39f69cafc63d3b469d591f702cab43ccabb22bc24
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/Y3KTKMRXB2BJKQ5SJOZZ62OK7R \
| 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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