pith:ZXVB6DF3
Design and Performance Studies of a Granular Thin HCAL-MuID Detector for the EIC Optimized for AI-Based Reconstruction
An iron-scintillator calorimeter with multi-dimensional readout measures neutral hadron momentum to a few tens of percent at the EIC using time of flight at lower energies and improved calorimetry at higher energies.
arxiv:2511.08432 v4 · 2025-11-11 · physics.ins-det · hep-ex
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Claims
for lower energies, excellent relative momentum measurements of a few 10% are achieved using time of flight, while for higher particle momenta, the energy can be measured calorimetrically with a resolution significantly better than that demonstrated for similar calorimeters read out with less granularity.
The foreseen excellent timing resolution in the multi-dimensional readout can be achieved in practice and that machine learning optimization of the design will deliver the claimed performance gains in real data.
The hKLM design uses highly segmented readout and machine learning optimization to deliver few-10% momentum resolution for low-energy neutral hadrons via time-of-flight and improved calorimetric energy resolution at higher energies compared to less granular systems.
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Receipt and verification
| First computed | 2026-05-29T02:05:37.747669Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
cdea1f0cbb5334021728383d1f12898a7fff7a401c3d221f7ece588252a245a0
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ZXVB6DF3KM2AEFZIHA6R6EUJRJ \
| 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: cdea1f0cbb5334021728383d1f12898a7fff7a401c3d221f7ece588252a245a0
Canonical record JSON
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