Pith Number

pith:7ZBIHT5I

pith:2025:7ZBIHT5IPJ3CRPZ4PIYBLERMEP

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E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features

Adrita Khan, AKM Mahbubur Rahman, Amin Ahsan Ali, Choudhury Ben Yamin Siddiqui, M. Arshad Momen, Md Raqibul Islam, Md. Zakir Hossan, Mir Sazzat Hossain, Tanjib Khan

E-PCN classifies jets by building four graphs each weighted by a different kinematic variable and uses Grad-CAM to show angular separation plus transverse momentum drive 76 percent of decisions.

arxiv:2512.07420 v2 · 2025-12-08 · hep-ph · cs.LG · hep-ex

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\pithnumber{7ZBIHT5IPJ3CRPZ4PIYBLERMEP}

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Record completeness

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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

Evaluated on the JetClass dataset with 10 signal classes, E-PCN achieves a macro-accuracy of 94.67%, macro-AUC of 96.78%, and macro-AUPR of 86.79%, representing improvements of 2.36%, 4.13%, and 24.88% respectively over the baseline PCN implementation, while demonstrating physically interpretable feature learning with angular separation and transverse momentum accounting for approximately 76% of classification decisions.

C2weakest assumption

That Grad-CAM attributions on the four kinematic-weighted graphs faithfully reflect the true causal importance of those variables in the model's internal decision process rather than explanation artifacts.

C3one line summary

E-PCN reaches 94.67% macro-accuracy on 10-class jet tagging by weighting graphs with angular separation, transverse momentum, momentum fraction, and invariant mass, with Grad-CAM showing the first two account for 76% of decisions and yielding gains over baseline PCN.

References

51 extracted · 51 resolved · 14 Pith anchors

[1] CERN Yellow Reports: Monographs 2020 · doi:10.23731/cyrm-2020-0010

[2] High-Luminosity LHC 2018

[3] S. Mondal and L. Mastrolorenzo,Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC,Eur. Phys. J. ST233(2024) 2657 [arXiv:2404.01071] [inSPIRE]. – 16 – 2024

[4] Machine Learning in High Energy Physics Community White Paper 2018 · arXiv:1807.02876

[5] H. Qu and L. Gouskos,ParticleNet: Jet Tagging via Particle Clouds,Phys. Rev. D101 (2020) 056019 [arXiv:1902.08570] [inSPIRE] 2020

Formal links

2 machine-checked theorem links

Cited by

1 paper in Pith

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

Receipt and verification

First computed	2026-05-21T01:04:19.880953Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

fe4283cfa87a7628bf3c7a3015922c23d507ea885be200feef5c5cc299028cb8

Aliases

arxiv: 2512.07420 · arxiv_version: 2512.07420v2 · doi: 10.48550/arxiv.2512.07420 · pith_short_12: 7ZBIHT5IPJ3C · pith_short_16: 7ZBIHT5IPJ3CRPZ4 · pith_short_8: 7ZBIHT5I

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/7ZBIHT5IPJ3CRPZ4PIYBLERMEP \
  | 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: fe4283cfa87a7628bf3c7a3015922c23d507ea885be200feef5c5cc299028cb8

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "43ab21b14fd439f59d49c5d6f72d3e33c5f123494caf36bd8ec33b3770b54ce6",
    "cross_cats_sorted": [
      "cs.LG",
      "hep-ex"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "hep-ph",
    "submitted_at": "2025-12-08T10:53:05Z",
    "title_canon_sha256": "764c76a6fd007d048c0822e905fc534da5938263774f16ce47854adb248ec187"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2512.07420",
    "kind": "arxiv",
    "version": 2
  }
}