Performance of b Jet Identification at $\sqrt{s}$ = 13 TeV with the ATLAS Detector at CERN [Slides] Article Swipe
The efficient identification of jets from bottom quarks (b-jets) is one of the most important techniques for many physics analyses at the Large Hadron Collider, including studies of the Higgs boson, the top quark, and searches beyond the Standard Model. The performance is characterized by b-tagging efficiency (probability to identify a b-jet as such) and the mistag rate (probability to mistakenly accept a non-b-jet).The mistag occurs as a result of finite detector resolution, presence of long-lived particles, and material interactions. As these effects can be different between the experimental data and Monte Carlo (MC) simulation, it is important to measure the b-tagging performance in data and derive the MC correction factors. I will describe various methods to measure the performance of b jet identification in pp collisions at √s = 13 TeV with the ATLAS detector and will report on Direct Tag method which is recently developed in the ATLAS collaboration for Light jet calibration.
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- Type
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- Language
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- Landing Page
- https://doi.org/10.2172/1462088
- OA Status
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W2993742552Canonical identifier for this work in OpenAlex
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https://doi.org/10.2172/1462088Digital Object Identifier
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Performance of b Jet Identification at $\sqrt{s}$ = 13 TeV with the ATLAS Detector at CERN [Slides]Work title
- Type
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reportOpenAlex work type
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enPrimary language
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2018Year of publication
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2018-07-30Full publication date if available
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W. IslamList of authors in order
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.osti.gov/biblio/1462088Direct OA link when available
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Large Hadron Collider, Physics, Particle physics, Atlas (anatomy), ATLAS experiment, Higgs boson, Jet (fluid), Detector, Nuclear physics, Atlas detector, Monte Carlo method, Measure (data warehouse), Quark, Physics beyond the Standard Model, Top quark, Hadron, Computer science, Data mining, Optics, Statistics, Paleontology, Thermodynamics, Mathematics, BiologyTop concepts (fields/topics) attached by OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.factors. | 110 |
| abstract_inverted_index.identify | 49 |
| abstract_inverted_index.material | 78 |
| abstract_inverted_index.presence | 73 |
| abstract_inverted_index.recently | 145 |
| abstract_inverted_index.searches | 35 |
| abstract_inverted_index.Collider, | 24 |
| abstract_inverted_index.b-tagging | 45, 101 |
| abstract_inverted_index.developed | 146 |
| abstract_inverted_index.different | 85 |
| abstract_inverted_index.efficient | 1 |
| abstract_inverted_index.important | 14, 97 |
| abstract_inverted_index.including | 25 |
| abstract_inverted_index.collisions | 126 |
| abstract_inverted_index.correction | 109 |
| abstract_inverted_index.efficiency | 46 |
| abstract_inverted_index.long-lived | 75 |
| abstract_inverted_index.mistakenly | 60 |
| abstract_inverted_index.particles, | 76 |
| abstract_inverted_index.techniques | 15 |
| abstract_inverted_index.performance | 41, 102, 119 |
| abstract_inverted_index.resolution, | 72 |
| abstract_inverted_index.simulation, | 94 |
| abstract_inverted_index.√s | 128 |
| abstract_inverted_index.(probability | 47, 58 |
| abstract_inverted_index.calibration. | 154 |
| abstract_inverted_index.experimental | 88 |
| abstract_inverted_index.characterized | 43 |
| abstract_inverted_index.collaboration | 150 |
| abstract_inverted_index.interactions. | 79 |
| abstract_inverted_index.identification | 2, 123 |
| abstract_inverted_index.non-b-jet).The | 63 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5080699493 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I1282105669 |
| citation_normalized_percentile |