Thea Klaeboe Aarrestad

发表

Unsupervised New Physics detection at 40 MHz: Training Dataset

Maurizio Pierini, Jennifer Ngadiuba, Kinga Anna Wozniak, 2021 .

The CMS Phase-1 pixel detector upgrade

A. Tsirou, Federico Faccio, A. Marchioro, 2021, Journal of Instrumentation.

Unsupervised New Physics detection at 40 MHz: LQ -> b tau Signal Benchmark Dataset

Maurizio Pierini, Jennifer Ngadiuba, Kinga Anna Wozniak, 2021 .

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Yi Wang, Andrea Benaglia, Yi Zhang, 2020, Journal of Instrumentation.

Performance of the CMS Level-1 trigger in proton-proton collisions at √s = 13 TeV

Yi Wang, Andrea Benaglia, Ian Watson, 2020 .

Search for resonant tt production in proton-proton collisions at √s = 8 TeV

Andrea Benaglia, Yu-Wei Chang, Marco Rossini, 2015, 1506.03062.

Search for a massive resonance decaying to a pair of Higgs bosons in the four b quark ﬁnal state in proton–proton collisions at √ s = 13 TeV

Andrea Benaglia, Francesco Fienga, Stavros Mallios, 2018 .

Search for new physics in final states with an energetic jet or a hadronically decaying W or Z boson and transverse momentum imbalance at √s=13 TeV

Yi Wang, Andrea Benaglia, Francesco Fienga, 2018 .

Performance of the CMS muon detector and muon reconstruction with proton-proton collisions at √s=13 TeV

Yi Wang, Andrea Benaglia, Francesco Fienga, 2018, Journal of Instrumentation.

Reconstruction and identification of tau lepton decays to hadrons and nu(tau) at CMS

Andrea Benaglia, Yu-Wei Chang, Marco Rossini, 2016 .

Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s}=$ 13 TeV

Yi Wang, Andrea Benaglia, Ian Watson, 2020 .

Performance of reconstruction and identification of τ leptons decaying to hadrons and vτ in pp collisions at √s=13 TeV

Yi Wang, Andrea Benaglia, Francesco Fienga, 2018, Journal of Instrumentation.

A deep neural network to search for new long-lived particles decaying to jets

Yi Wang, Andrea Benaglia, Ian Watson, 2019 .