Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Rahmani, Mehdi"'
Autor:
Moshtagh, Shiva, Rahmani, Mehdi
This paper introduces a two-level robust approach to estimate the unknown states of a large-scale power system while the measurements and network parameters are subjected to uncertainties. The bounded data uncertainty (BDU) considered in the power ne
Externí odkaz:
http://arxiv.org/abs/2212.06309
Illuminating long-lived dark vector bosons via exotic Higgs decays at $\sqrt{s} = 13\,{\text {TeV}}$
Autor:
Elkafrawy, Tamer, Hohlmann, Marcus, Kamon, Teruki, Padley, Paul, Kim, Hyunyong, Rahmani, Mehdi, Dildick, Sven
The possibility of producing a measurable long-lived dark $Z$ boson, that is assumed to be produced either via its kinetic mixing with the hypercharge gauge boson or via mixing of the observed 125-GeV Higgs boson with the dark Higgs boson, called Hig
Externí odkaz:
http://arxiv.org/abs/2111.03960
Automated ranking of pre-trained Deep Neural Networks (DNNs) reduces the required time for selecting optimal pre-trained DNN and boost the classification performance in transfer learning. In this paper, we introduce a novel algorithm to rank pre-trai
Externí odkaz:
http://arxiv.org/abs/2012.13717
Autor:
Hohlmann, Marcus, Bomberger, Matthew, Colafranceschi, Stefano, Jimenez, Francisco, Rahmani, Mehdi, Zhang, Aiwu
We present design and construction of a large low-mass Triple-GEM detector prototype for forward tracking at a future Electron-Ion Collider. In this environment, multiple scattering of forward and backward tracks must be minimized so that electron tr
Externí odkaz:
http://arxiv.org/abs/1711.05333