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DC Field | Value | Language |
---|---|---|
dc.contributor.author | CMS Collaboration | en_US |
dc.contributor.author | Sirunyan, A. M. | en_US |
dc.contributor.author | DUBE, SOURABH | en_US |
dc.contributor.author | KANSAL, B. | en_US |
dc.contributor.author | KAPOOR, A. | en_US |
dc.contributor.author | KOTHEKAR, K. | en_US |
dc.contributor.author | PANDEY, S. | en_US |
dc.contributor.author | RANE, A. | en_US |
dc.contributor.author | RASTOGI, A. | en_US |
dc.contributor.author | SHARMA, SEEMA et al. | en_US |
dc.date.accessioned | 2020-07-24T05:59:05Z | |
dc.date.available | 2020-07-24T05:59:05Z | |
dc.date.issued | 2020-06 | en_US |
dc.identifier.citation | Journal of Instrumentation, 15(6). | en_US |
dc.identifier.issn | 1748-0221 | en_US |
dc.identifier.uri | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/4896 | - |
dc.identifier.uri | https://doi.org/10.1088/1748-0221/15/06/P06005 | en_US |
dc.description.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 √s = 13TeV, 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. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOP Publishing | en_US |
dc.subject | CMS | en_US |
dc.subject | Physics | en_US |
dc.subject | TOC-JUL-2020 | en_US |
dc.subject | 2020 | en_US |
dc.subject | 2020-JUL-WEEK4 | en_US |
dc.title | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques | en_US |
dc.type | Article | en_US |
dc.contributor.department | Dept. of Physics | en_US |
dc.identifier.sourcetitle | Journal of Instrumentation | en_US |
dc.publication.originofpublisher | Foreign | en_US |
Appears in Collections: | JOURNAL ARTICLES |
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