Identification of heavy, energetic hadronically decaying particles using machine-learning techniques
- Title
- Identification of heavy, energetic hadronically decaying particles using machine-learning techniques
- Author
- 김태정
- Keywords
- Large detector-systems performance; Pattern recognition, cluster finding, calibration and fitting methods
- Issue Date
- 2020-06
- Publisher
- IOP PUBLISHING LTD
- Citation
- JOURNAL OF INSTRUMENTATION, v. 15, no. 6, article no. P06005
- Abstract
- Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at root S = 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1). Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
- URI
- https://iopscience.iop.org/article/10.1088/1748-0221/15/06/P06005https://repository.hanyang.ac.kr/handle/20.500.11754/168842
- ISSN
- 1748-0221
- DOI
- 10.1088/1748-0221/15/06/P06005
- Appears in Collections:
- COLLEGE OF NATURAL SCIENCES[S](자연과학대학) > PHYSICS(물리학과) > Articles
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