Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques
| dc.WoS.categories | Instruments & Instrumentation | en_US |
| dc.authorid | 0000-0002-9007-8260 | en_US |
| dc.contributor.author | Yetkin, Elif Aslı | |
| dc.date.accessioned | 2020-12-08T07:03:21Z | |
| dc.date.available | 2020-12-08T07:03:21Z | |
| dc.date.issued | 2020-06 | |
| dc.department | Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Enerji Sistemleri Mühendisliği Bölümü | en_US |
| dc.description | 88 pages | 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 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. | en_US |
| dc.fullTextLevel | Full Text | en_US |
| dc.identifier.doi | 10.1088/1748-0221/15/06/P06005 | |
| dc.identifier.issn | 1748-0221 | |
| dc.identifier.uri | https://hdl.handle.net/11411/2781 | |
| dc.identifier.uri | https://doi.org/10.1088/1748-0221/15/06/P06005 | |
| dc.identifier.wos | WOS:000545350900005 | en_US |
| dc.identifier.wosquality | Q3 | en_US |
| dc.indekslendigikaynak | Web of Science | en_US |
| dc.issue | 6 | en_US |
| dc.language.iso | en | en_US |
| dc.national | International | en_US |
| dc.numberofauthors | 1000+ | en_US |
| dc.publisher | IOP Publishing Ltd. | en_US |
| dc.relation.ispartof | Journal of Instrumentation | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Pattern recognition, cluster finding, calibration and fitting methods | en_US |
| dc.subject | Large detector-systems performance | en_US |
| dc.title | Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques | |
| dc.type | Article | |
| dc.volume | 15 | en_US |
