pith:2NAD3FOD
Higgs Physics with the XFEL Compton $\boldsymbol{\gamma\gamma}$ Collider Concept at $\boldsymbol{\sqrt{s}=125}$ GeV
An XFEL-based γγ collider at 125 GeV can measure Higgs properties with high precision by applying set transformer machine learning to particle data.
arxiv:2605.16756 v1 · 2026-05-16 · hep-ph · hep-ex
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Claims
Our results demonstrate that an XFEL γγ collider can probe the Higgs sector with extremely high precision and enable new physics opportunities, complementary to proposed e+e- machines.
The set transformer-based deep learning framework that acts on particle-flow object point clouds, when coupled with a genetic algorithm optimizer, yields significantly higher sensitivity than traditional methods for signal-background discrimination in the Higgs final states.
An XFEL Compton gamma-gamma collider at 125 GeV with a set transformer deep learning classifier on particle-flow point clouds can achieve high-precision Higgs measurements across hadronic, semi-leptonic, and leptonic final states including H to strange quarks.
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| First computed | 2026-05-20T00:03:20.099271Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/2NAD3FODCPJKAEICZCFPWUTESC \
| 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: d3403d95c313d2a01102c88afb5264908d78cac4cb3bc46013f95f89c21e827d
Canonical record JSON
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