Pith Number

pith:GBDZ5PEJ

pith:2026:GBDZ5PEJFSERSMS2JC44ZUILVG

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Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector

Jay Chan

Double Metric Learning resolves contrastive loss conflicts in chain connections by learning two node representations for directed graph construction.

arxiv:2605.14131 v1 · 2026-05-13 · physics.data-an · hep-ex · hep-ph

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Claims

C1strongest claim

We test this idea with the ATLAS ITk detector at the HL-LHC using the ATLAS ITk simulation and show better graph construction performance particularly for particles with high transverse momentum compared to the Simple Metric Learning approach. We also show that Double Metric Learning is able to accurately predict edge direction.

C2weakest assumption

That learning two independent node representations resolves the contrastive loss conflict for chain connections without introducing new overfitting or bias that would degrade overall tracking performance on real data.

C3one line summary

Double metric learning learns two embeddings per node to build directed graphs with chain connections, yielding better performance than single metric learning for high-pT particles and accurate edge direction prediction in ATLAS ITk simulations.

References

21 extracted · 21 resolved · 3 Pith anchors

[1] Track and vertex reconstruction: From classical to adaptive methods , author =. Rev. Mod. Phys. , volume =. 2010 , month =. doi:10.1103/RevModPhys.82.1419 , url = 2010 · doi:10.1103/revmodphys.82.1419

[2] Performance of the ATLAS Track Reconstruction Algorithms in Dense Environments in LHC Run 2 2017 · doi:10.1140/epjc/s10052-017-5225-7

[3] Description and performance of track and primary-vertex reconstruction with the CMS tracker 2014 · doi:10.1088/1748-0221/9/10/p10009

[4] Optimizations of the ATLAS ITk GNN reconstruction pipeline. 2025 2025

[5] Performance of a geometric deep learning pipeline for HL-LHC particle tracking 2021 · doi:10.1140/epjc/s10052-021-09675-8

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:11.790494Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

30479ebc892c8919325a48b9ccd10ba99804f4a7372f7abcf71d1391ff30f6db

Aliases

arxiv: 2605.14131 · arxiv_version: 2605.14131v1 · doi: 10.48550/arxiv.2605.14131 · pith_short_12: GBDZ5PEJFSER · pith_short_16: GBDZ5PEJFSERSMS2 · pith_short_8: GBDZ5PEJ

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/GBDZ5PEJFSERSMS2JC44ZUILVG \
  | 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: 30479ebc892c8919325a48b9ccd10ba99804f4a7372f7abcf71d1391ff30f6db

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "54007406f6792fff263e7605115b0d4b4e2491ad474314140829420a6488d3bf",
    "cross_cats_sorted": [
      "hep-ex",
      "hep-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "physics.data-an",
    "submitted_at": "2026-05-13T21:31:28Z",
    "title_canon_sha256": "e7b961cce184f4f46eaa357b197b098d2e2d63f898ea44c047952843de799772"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14131",
    "kind": "arxiv",
    "version": 1
  }
}