Pith Number

pith:JUC6HRTI

pith:2026:JUC6HRTIMEORLGERTIQ6GGQISR

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Design and performance of a large-area scintillator-based chamber for the MID subsystem of ALICE 3

Antonio Ortiz, Antonio Paz, Edmundo Garc\'ia Solis, Gerardo Herrera Corral, Guillermo Tejeda Mu\~noz, Ian P\'erez Garc\'ia, Ildefonso Le\'on Monz\'on, Jes\'us Eduardo Mu\~noz M\'endez, Juan Carlos Cabanillas Noris, Laura Helena Gonz\'alez Trueba, Paola Vargas Torres, Ricardo Rodr\'iguez Pineda, Ruben Alfaro Molina, Solangel Rojas Torres, Varlen Grabski, Victor V\'azquez Campos, Yael Antonio V\'asquez Beltran

A two-layer scintillator chamber for ALICE 3 muon identification achieves over 99 percent muon efficiency using machine learning on beam data.

arxiv:2604.00176 v1 · 2026-03-31 · physics.ins-det · hep-ex

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Record completeness

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4 Citations open

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Claims

C1strongest claim

The trained ML algorithm was applied to muon data, yielding a muon efficiency above 99% for the OR condition (detection in either layer 1 or 2). The implementation in the pion-beam data gives the fake-muon efficiency as a function of the absorber length that is well described by an exponential function with a slope parameter of 18.79 cm.

C2weakest assumption

The 50 percent data split used for training and testing the ML classifier is representative of the full dataset and of the particle rates and backgrounds expected in the final ALICE 3 environment; no significant overfitting or beam-specific artifacts affect the quoted efficiencies.

C3one line summary

A scintillator-based prototype chamber for ALICE 3 MID delivers above 99 percent muon efficiency and an exponentially falling pion fake rate with 18.79 cm attenuation length in beam tests.

References

10 extracted · 10 resolved · 2 Pith anchors

[1] Acharya, et al., The ALICE experiment: a journey through QCD, Eur 2024 · doi:10.1140/epjc/s10052-024-12935-y

[2] Heavy-ion physics at the LHC: Review of Run I results 2016 · doi:10.1142/s0218301316420064

[3] Letter of intent for ALICE 3: A next-generation heavy- ion experiment at the LHC 2022

[4] A. Dainese, A. Di Mauro, Scoping document for the ALICE 3 detector, Tech. rep., CERN, Geneva (2025). URLhttps://cds.cern.ch/record/2925455 2025

[5] J. E. Muñoz Méndez, et al., ML-based muon identification using a FNAL-NICADD scintillator chamber for the MID subsystem of ALICE 3, JINST 20 (09) (2025) P09015.arXiv:2507.02817,doi:10.1088/ 1748-0221/ 2025

Receipt and verification

First computed	2026-05-18T02:44:30.585292Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

4d05e3c668611d1598919a21e31a08944e45b142067f05e2421029d24f0f420a

Aliases

arxiv: 2604.00176 · arxiv_version: 2604.00176v1 · doi: 10.48550/arxiv.2604.00176 · pith_short_12: JUC6HRTIMEOR · pith_short_16: JUC6HRTIMEORLGER · pith_short_8: JUC6HRTI

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/JUC6HRTIMEORLGERTIQ6GGQISR \
  | 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: 4d05e3c668611d1598919a21e31a08944e45b142067f05e2421029d24f0f420a

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "75129849b0b0723d1bb74ead23e601f61a729481fb62192369184c7eeb33ed30",
    "cross_cats_sorted": [
      "hep-ex"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
    "primary_cat": "physics.ins-det",
    "submitted_at": "2026-03-31T19:28:16Z",
    "title_canon_sha256": "bea5950c10cb8d02ddc67cc07d8717a361a7d13c0750037f83f6494b99d85196"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.00176",
    "kind": "arxiv",
    "version": 1
  }
}