{
  "annotation_id": "90147ffe-4f47-437a-90f3-e71dc8f5a3ce",
  "date_created": "2026-03-02T18:01:18.084000Z",
  "date_modified": "2026-03-02T18:01:18.084000Z",
  "file_hash": "31d655b7e4455f6be476a1ec68b77d09bde57a0c13cc5743fa3a816231aa2e4a",
  "private": false,
  "record": {
    "abstract": "This paper evaluates the performance of boosted decision trees for tagging\nb-jets. It is shown, using a Monte Carlo simulation of $WH \\to l\\nu q\\bar{q}$\nevents that boosted decision trees outperform feed-forward neural networks. The\nresults show that for a b-tagging efficiency of 60% the light jet rejection\ngiven by boosted decision trees is about 35% higher than that given by neural\nnetworks.",
    "arxiv_id": "physics/0702041",
    "authors": [
      "J. Bastos"
    ],
    "categories": [
      "physics.data-an",
      "hep-ex"
    ],
    "title": "Tagging heavy flavours with boosted decision trees",
    "url": "https://arxiv.org/abs/physics/0702041"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "230c722b-2eab-4b35-b9f4-2ca796260303",
    "id": "arXiv Dataset IDs",
    "type": "Model",
    "variant": "snapshot-2026-03-01",
    "version": "0.1.0"
  },
  "user_id": 1000002
}