dorsal/arxiv
View SchemaTagging heavy flavours with boosted decision trees
| Authors | J. Bastos |
|---|---|
| Categories | |
| ArXiv ID | physics/0702041 |
| URL | https://arxiv.org/abs/physics/0702041 |
Abstract
This paper evaluates the performance of boosted decision trees for tagging b-jets. It is shown, using a Monte Carlo simulation of $WH \to l\nu q\bar{q}$ events that boosted decision trees outperform feed-forward neural networks. The results show that for a b-tagging efficiency of 60% the light jet rejection given by boosted decision trees is about 35% higher than that given by neural networks.
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"date_created": "2026-03-02T18:01:18.084000Z",
"date_modified": "2026-03-02T18:01:18.084000Z",
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"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"
},
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