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pro vyhledávání: '"Vijay, Aravind H."'
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural net
Externí odkaz:
http://arxiv.org/abs/2010.11778
Autor:
Roy, Tuhin S., Vijay, Aravind H.
We propose a robust method to identify anomalous jets by vetoing QCD-jets. The robustness of this method ensures that the distribution of the proposed discriminating variable (which allows us to veto QCD-jets) remains unaffected by the phase space of
Externí odkaz:
http://arxiv.org/abs/1903.02032
Autor:
Godbole, Rohini, Guchait, Monoranjan, Khosa, Charanjit K., Lahiri, Jayita, Sharma, Seema, Vijay, Aravind H.
Publikováno v:
Phys. Rev. D 100, 056010 (2019)
In top quark production, the polarization of top quarks, decided by the chiral structure of couplings, is likely to be modified in the presence of any new physics contribution to the production. Hence the same is a good discriminator for those new ph
Externí odkaz:
http://arxiv.org/abs/1902.08096
Autor:
Guchait, Monoranjan, Vijay, Aravind H.
Publikováno v:
Phys. Rev. D 98, 115028 (2018)
Signature of heavier charged Higgs boson, much above the top quark mass, is investigated at the LHC Run 2 experiments, following its decay mode via top and bottom quark focusing on both hadronic and leptonic signal final states. The generic two Higgs
Externí odkaz:
http://arxiv.org/abs/1806.01317
The discovery potential of light pseudo scalar Higgs boson for the mass range 10-60 GeV is explored. In the context of the next-to-minimal supersymmetric standard(NMSSM) model, the branching fraction of light pseudo scalar Higgs boson decaying to a p
Externí odkaz:
http://arxiv.org/abs/1705.06275
Publikováno v:
Scientific Reports 5 Article Number: 10304, 2015
The superposition principle is usually incorrectly applied in interference experiments. This has recently been investigated through numerics based on Finite Difference Time Domain (FDTD) methods as well as the Feynman path integral formalism. In the
Externí odkaz:
http://arxiv.org/abs/1412.2198
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional neural net
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ea6342c0a5917e5a0d38bbfa597cb5da
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