pith:3ORNYHBJ
Learning holographic QCD with unflavoured meson spectra
A neural network reconstructs the five-dimensional holographic geometry and potentials of QCD from unflavored meson mass spectra.
arxiv:2512.16450 v2 · 2025-12-18 · hep-ph · cond-mat.dis-nn · hep-th
Add to your LaTeX paper
\usepackage{pith}
\pithnumber{3ORNYHBJFIDU45CUBJ2UXHDEFH}
Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge
Record completeness
Claims
Using the masses of the unflavored mesons ρ, a1, a2, and f0 and their excitations as training data, the model learns confining effective potentials and computes a dilaton profile that satisfies the null energy condition. The network predicts that the dilaton's IR behavior will be much steeper than its quadratic form. The symmetry-breaking bulk potential V(X)=k1 X^3 + k2 X^4 was computed with k1 ∼ -4 and k2 ∼ 9. The deep-learned parameters, metric, and dilaton profile were then used to predict the pion mass and its spectrum with good accuracy.
The discretized Schrödinger-like equation with Dirichlet boundary conditions on a linear moose accurately represents the holographic QCD dynamics, and the chosen set of meson masses is sufficient to determine the geometry and potentials uniquely without significant overfitting or degeneracy.
Neural network learns confining potentials and dilaton profile in holographic QCD from meson spectra, predicting steeper IR dilaton and pion masses with good accuracy.
References
Formal links
Receipt and verification
| First computed | 2026-05-18T02:44:32.111843Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
dba2dc1c292a074e74540a754b9c6429d6502e54fd36dc769eaaa146408ba112
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3ORNYHBJFIDU45CUBJ2UXHDEFH \
| 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: dba2dc1c292a074e74540a754b9c6429d6502e54fd36dc769eaaa146408ba112
Canonical record JSON
{
"metadata": {
"abstract_canon_sha256": "4705099805d11449ed95197989409e4436f80136e5aacc845b76899bb328fa40",
"cross_cats_sorted": [
"cond-mat.dis-nn",
"hep-th"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "hep-ph",
"submitted_at": "2025-12-18T12:11:16Z",
"title_canon_sha256": "72b19dd041276e2ce1c6c4838c76db849b830a1e6d54c49a4edfb6384ba756f3"
},
"schema_version": "1.0",
"source": {
"id": "2512.16450",
"kind": "arxiv",
"version": 2
}
}