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.
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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