Measurements of Inclusive Muon Neutrino and Antineutrino Charged Current Differential Cross Sections on Argon in the NuMI Antineutrino Beam

The ArgoNeuT collaboration Acciarri, R. ; Adams, C. ; Asaadi, J. ; et al.
Phys.Rev.D 89 (2014) 112003, 2014.
Inspire Record 1291281 DOI 10.17182/hepdata.64419

The ArgoNeuT collaboration presents measurements of inclusive muon neutrino and antineutrino charged current differential cross sections on argon in the Fermilab NuMI beam operating in the low energy antineutrino mode. The results are reported in terms of outgoing muon angle and momentum at a mean neutrino energy of 9.6 GeV (neutrinos) and 3.6 GeV (antineutrinos), in the range $0^\circ < \theta_\mu < 36^\circ$ and $0 < p_\mu < 25$ GeV/$c$, for both neutrinos and antineutrinos.

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Measurement of $K^{+}$ production in charged-current $\nu_{\mu}$ interactions

The MINERvA collaboration Marshall, C.M. ; Aliaga, L. ; Altinok, O. ; et al.
Phys.Rev.D 94 (2016) 012002, 2016.
Inspire Record 1446753 DOI 10.17182/hepdata.78539

Production of K^{+} mesons in charged-current \nu_{\mu} interactions on plastic scintillator (CH) is measured using MINERvA exposed to the low-energy NuMI beam at Fermilab. Timing information is used to isolate a sample of 885 charged-current events containing a stopping K^{+} which decays at rest. The differential cross section in K^{+} kinetic energy, d\sigma/dT_{K}, is observed to be relatively flat between 0 and 500 MeV. Its shape is in good agreement with the prediction by the \textsc{genie} neutrino event generator when final-state interactions are included, however the data rate is lower than the prediction by 15\%.

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Identification of hadronic tau lepton decays using a deep neural network

The CMS collaboration Tumasyan, Armen ; Adam, Wolfgang ; Andrejkovic, Janik Walter ; et al.
JINST 17 (2022) P07023, 2022.
Inspire Record 2016054 DOI 10.17182/hepdata.116281

A new algorithm is presented to discriminate reconstructed hadronic decays of tau leptons ($\tau_\mathrm{h}$) that originate from genuine tau leptons in the CMS detector against $\tau_\mathrm{h}$ candidates that originate from quark or gluon jets, electrons, or muons. The algorithm inputs information from all reconstructed particles in the vicinity of a $\tau_\mathrm{h}$ 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 $\tau_\mathrm{h}$ to pass the discriminator against jets increases by 10-30% for a given efficiency for quark and gluon jets. Furthermore, a more efficient $\tau_\mathrm{h}$ 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 $\tau_\mathrm{h}$ reconstruction method are validated with LHC proton-proton collision data at $\sqrt{s} =$ 13 TeV.

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