Yayınlanmış 1 Ocak 2022
| Sürüm v1
Dergi makalesi
Açık
Identification of hadronic tau lepton decays using a deep neural network
Oluşturanlar
- Tumasyan, A.1
- Adam, W.2
- Andrejkovic, J. W.2
- Bergauer, T.2
- Chatterjee, S.2
- Dragicevic, M.2
- Del Valle, A. Escalante2
- Fruhwirth, R.
- Jeitler, M.
- Krammer, N.2
- Lechner, L.2
- Liko, D.2
- Mikulec, I2
- Paulitsch, P.2
- Pitters, F. M.2
- Schieck, J.
- Schofbeck, R.2
- Schwarz, D.2
- Templ, S.2
- Waltenberger, W.2
- Waltenberger, W.2
- 1. Yerevan Phys Inst, Yerevan, Armenia
- 2. Inst Hochenergiephys, Vienna, Austria
Açıklama
A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons (tau(h)) that originate from genuine tau leptons in the CMS detector against tau(h) candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a tau(h) candidate and employs a deep neural network with convolutional layers to efficiently process the inputs. This algorithm leads to a significantly improved performance compared with the previously used one. For example, the efficiency for a genuine tau(h) to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient tau(h) reconstruction is introduced that incorporates additional hadronic decay modes. The superior performance of the new algorithm to discriminate against jets, electrons, and muons and the improved tau(h) reconstruction method are validated with LHC proton-proton collision data at root s = 13 TeV.
Dosyalar
bib-667a9804-a17b-4234-8bd8-4252d5abb4f8.txt
Dosyalar
(394 Bytes)
| Ad | Boyut | Hepisini indir |
|---|---|---|
|
md5:8249d211964557b6867b67299a83b2b1
|
394 Bytes | Ön İzleme İndir |