{
  "annotation_id": "fbcf98ac-6b92-45ca-85d3-6f5853670d8e",
  "date_created": "2026-02-17T05:53:11.554000Z",
  "date_modified": "2026-02-17T05:53:11.554000Z",
  "file_hash": "6c9e594e44d0647a1b38f263647d317be652c2ba93f2e5f48c045993c187385a",
  "private": false,
  "record": {
    "abstract": "We address the problem of inferring the location of a target that releases odor in the presence of turbulence. Input for the inference is provided by many sensors scattered within the odor plume. Drawing inspiration from distributed chemosensation in biology, we ask whether the accuracy of the inference is affected by proprioceptive noise, i.e., noise on the perceived location of the sensors. Surprisingly, in the presence of a net fluid flow, proprioceptive noise improves Bayesian inference, rather than degrading it. An optimal noise exists that efficiently leverages additional information hidden within the geometry of the odor plume. Empirical tuning of noise functions well across a range of distances and may be implemented in practice. Other sources of noise also improve accuracy, owing to their ability to break the spatiotemporal correlations of the turbulent plume. These counterintuitive benefits of noise may be leveraged to improve sensory processing in biology and robotics.",
    "arxiv_id": "2601.07445",
    "authors": [
      "Francesco Marcolli",
      "Martin James",
      "Agnese Seminara"
    ],
    "categories": [
      "physics.bio-ph",
      "cond-mat.stat-mech",
      "physics.comp-ph"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Noise enhances odor source localization",
    "url": "https://arxiv.org/abs/2601.07445",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "322b583b-5622-4ee2-97fe-ea71b4e1cce9",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}