pith:K3CQSV3U
Radioactive Source Seeking using Bayesian Optimisation with Movement Penalty
Bayesian optimisation with a movement penalty and heteroscedastic Gaussian process localises radioactive sources while achieving sublinear regret.
arxiv:2605.14942 v1 · 2026-05-14 · physics.app-ph · cs.SY · eess.SY
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\pithnumber{K3CQSV3UZ44UJ7UIBX7LO2RURC}
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Record completeness
Claims
The strategy is shown to generate sublinear regret in the source-seeking task, while simulations demonstrate its effectiveness in localising radioactive sources.
That the radiation intensity field can be adequately modeled by a heteroscedastic Gaussian process whose noise structure remains stable enough for the regret bound to hold in realistic environments.
A Bayesian optimization method with heteroscedastic Gaussian process surrogate and movement switching cost achieves sublinear regret and effective source localization in simulations for radioactive source seeking.
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Formal links
Receipt and verification
| First computed | 2026-05-17T23:38:55.456192Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
56c5095774cf3944fe880dfeb76a3488baa6b94dc1ff888f005c74d51066b421
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/K3CQSV3UZ44UJ7UIBX7LO2RURC \
| 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: 56c5095774cf3944fe880dfeb76a3488baa6b94dc1ff888f005c74d51066b421
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
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"submitted_at": "2026-05-14T15:16:33Z",
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