Pith Number

pith:OWVZPWDY

pith:2026:OWVZPWDYVBIKNGZABCRU7WB6EA

not attested not anchored not stored refs resolved

Integrating Bayesian Spectral Deconvolution and Expert Scientific Reasoning for Robust Peak Estimation

Evan Jamison, Hayato Okubo, Hiroyuki Kumazoe, Satoshi Tanaka, Shiryu Nakano, Toshimitsu Aritake, Yoh-ichi Mototake, Yoshifumi Amamoto

Averaging physical-property likelihoods over Bayesian-inferred spectra selects models consistent with measured material properties.

arxiv:2605.17518 v1 · 2026-05-17 · physics.data-an · stat.AP · stat.ML

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{OWVZPWDYVBIKNGZABCRU7WB6EA}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

By averaging the physical-property likelihood over posterior predictive spectra inferred from Bayesian spectral deconvolution, the proposed method selects spectral models according to the consistency between inferred spectral structures and physical-property information.

C2weakest assumption

The physical-property regression layer implemented via Gaussian process regression supplies an independent and reliable consistency signal that correctly identifies physically meaningful peaks even when spectrum-only Bayesian deconvolution fails or misidentifies them.

C3one line summary

The method couples Bayesian spectral deconvolution with a Gaussian process physical-property regression layer to select peak models consistent with auxiliary measurements, recovering meaningful structures missed by spectrum-only inference.

References

45 extracted · 45 resolved · 0 Pith anchors

[1] The regression of spectral data using the spectral deconvo- lution model yields information such as the number of peaks, peak positions, and peak variances [10]

[2] We employed Gaussian process regression as the Bayesian physical-property regression model in this study

[3] The simplest physical prior knowledge is the value of a property related to a spectrum

[4] Marginalization of spectral deconvolution model The method for sampling from the posterior of the spectral deconvolution model using Replica Exchange Monte Carlo is described here. The purpose of the

[5] Marginalization of Physical-Property Regression Models This section describes the marginalization calculation for the physical-property regression model defined in Sec. II B 2. The spectral samples{𝑌

Receipt and verification

First computed	2026-05-20T00:04:43.398709Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

75ab97d878a850a69b2008a34fd83e2002d142b072758834958727709d1aeab2

Aliases

arxiv: 2605.17518 · arxiv_version: 2605.17518v1 · doi: 10.48550/arxiv.2605.17518 · pith_short_12: OWVZPWDYVBIK · pith_short_16: OWVZPWDYVBIKNGZA · pith_short_8: OWVZPWDY

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/OWVZPWDYVBIKNGZABCRU7WB6EA \
  | 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: 75ab97d878a850a69b2008a34fd83e2002d142b072758834958727709d1aeab2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "6b3e6efe5bb43abea2f42827541a9a3e0b5b4f8218d2ccdfd81a2646e6033663",
    "cross_cats_sorted": [
      "stat.AP",
      "stat.ML"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "physics.data-an",
    "submitted_at": "2026-05-17T16:03:13Z",
    "title_canon_sha256": "6f0b3ba382d8a1aeff5613e0bdf3609dcd720e280338324bf81e8a8d8e8374ea"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.17518",
    "kind": "arxiv",
    "version": 1
  }
}