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Abstract Measuring observables to constrain models using maximum-likelihood estimation is fundamental to many physics experiments. Wilks' theorem provides a simple way to construct confidence intervals on model parameters, but it only applies under certain conditions. These conditions, such as nested hypotheses and unbounded parameters, are often violated in neutrino oscillation measurements and other experimental scenarios. Monte Carlo methods can address these issues, albeit at increased computational cost. In the presence of nuisance parameters, however, the best way to implement a Monte Carlo method is ambiguous. This paper documents the method selected by the NOvA experiment, the profile construction. It presents the toy studies that informed the choice of method, details of its implementation, and tests performed to validate it. It also includes some practical considerations which may be of use to others choosing to use the profile construction. 

Abstract A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ_h) from quark or gluon jets and electrons and muons that are misreconstructed as τ_hcandidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τ_hcandidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ_hcandidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTaualgorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb^-1, respectively. Techniques to calibrate the performance of the τ_h  identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses. 

Abstract Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.