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The noise radiated from ships can be used for their identification and classification using passive sonar systems. Several techniques have been proposed for military ship classification based on acoustic signatures, which can be acquired through controlled experiments performed in an acoustic lane. The cost for such data acquisition is a significant issue since the ship and crew have to be dislocated from the fleet. In addition, the experiments have to be repeated for different operational conditions, taking a considerable amount of time. Even with this massive effort, the scarce amount of data produced by these controlled experiments may limit further detailed analyses. In this paper, deep learning models are used for full exploitation of such acquired data, envisaging passive sonar signal classification. A drawback of such models is the large number of parameters, which requires extensive data volumes for parameter tuning along the training phase. Thus, generative adversarial networks (GANs) are used to synthesize data so that a larger data volume can be produced for training convolutional neural networks (CNNs), which are used for the classification task. Different GAN design approaches were evaluated and both maximum probability and class-expert strategies were exploited for signal classification. Special attention was paid to how the expert knowledge might give a handle on analyzing the performance of the various deep learning models through tests that mirrored actual deployment. An accuracy as high as 99.0±0.4% was achieved using experimental data, which improves upon previous machine learning designs in the field.

This paper presents direct searches for lepton flavour violation in Higgs boson decays, H → eτ and H → μτ, performed using data collected with the ATLAS detector at the LHC. The searches are based on a data sample of proton-proton collisions at a centre-of-mass energy $$ \sqrt{s} $$ s = 13 TeV, corresponding to an integrated luminosity of 138 fb−1. Leptonic (τ → ℓνℓντ) and hadronic (τ → hadrons ντ) decays of the τ-lepton are considered. Two background estimation techniques are employed: the MC-template method, based on data-corrected simulation samples, and the Symmetry method, based on exploiting the symmetry between electrons and muons in the Standard Model backgrounds. No significant excess of events is observed and the results are interpreted as upper limits on lepton-flavour-violating branching ratios of the Higgs boson. The observed (expected) upper limits set on the branching ratios at 95% confidence level, $$ \mathcal{B} $$ B (H → eτ) < 0.20% (0.12%) and $$ \mathcal{B} $$ B (H → μτ ) < 0.18% (0.09%), are obtained with the MC-template method from a simultaneous measurement of potential H → eτ and H → μτ signals. The best-fit branching ratio difference, $$ \mathcal{B} $$ B (H → μτ) → $$ \mathcal{B} $$ B (H → eτ), measured with the Symmetry method in the channel where the τ-lepton decays to leptons, is (0.25 ± 0.10)%, compatible with a value of zero within 2.5σ.

Differential and double-differential distributions of kinematic variables of leptons from decays of top-quark pairs ($$ t\overline{t} $$ t t ¯ ) are measured using the full LHC Run 2 data sample collected with the ATLAS detector. The data were collected at a pp collision energy of $$ \sqrt{s} $$ s = 13 TeV and correspond to an integrated luminosity of 140 fb−1. The measurements use events containing an oppositely charged eμ pair and b-tagged jets. The results are compared with predictions from several Monte Carlo generators. While no prediction is found to be consistent with all distributions, a better agreement with measurements of the lepton pT distributions is obtained by reweighting the $$ t\overline{t} $$ t t ¯ sample so as to reproduce the top-quark pT distribution from an NNLO calculation. The inclusive top-quark pair production cross-section is measured as well, both in a fiducial region and in the full phase-space. The total inclusive cross-section is found to be$$ {\sigma}_{t\overline{t}}=829\pm 1\ \left(\textrm{stat}\right)\pm 13\ \left(\textrm{syst}\right)\pm 8\ \left(\textrm{lumi}\right)\pm 2\ \left(\textrm{beam}\right)\ \textrm{pb}, $$ σ t t ¯ = 829 ± 1 stat ± 13 syst ± 8 lumi ± 2 beam pb , where the uncertainties are due to statistics, systematic effects, the integrated luminosity and the beam energy. This is in excellent agreement with the theoretical expectation.

In a special run of the LHC with $$\beta ^{\star } = 2.5$$ β ⋆ = 2.5  km, proton–proton elastic-scattering events were recorded at $$\sqrt{s} = 13$$ s = 13  TeV with an integrated luminosity of $$340~\upmu {\text {b}}^{-1}$$ 340 μ b - 1 using the ALFA subdetector of ATLAS in 2016. The elastic cross section was measured differentially in the Mandelstam t variable in the range from $$-t = 2.5 \cdot 10^{-4}$$ - t = 2.5 · 10 - 4  GeV$$^{2}$$ 2 to $$-t = 0.46$$ - t = 0.46  GeV$$^{2}$$ 2 using 6.9 million elastic-scattering candidates. This paper presents measurements of the total cross section $$\sigma _{\text {tot}}$$ σ tot , parameters of the nuclear slope, and the $$\rho $$ ρ -parameter defined as the ratio of the real part to the imaginary part of the elastic-scattering amplitude in the limit $$t \rightarrow 0$$ t → 0 . These parameters are determined from a fit to the differential elastic cross section using the optical theorem and different parameterizations of the t-dependence. The results for $$\sigma _{\text {tot}}$$ σ tot and $$\rho $$ ρ are $$\begin{aligned} \sigma _{\text {tot}}(pp\rightarrow X) = 104.7 \pm 1.1 \; \text{ mb },\quad \rho = 0.098 \pm 0.011 . \end{aligned}$$ σ tot ( p p → X ) = 104.7 ± 1.1 mb , ρ = 0.098 ± 0.011 . The uncertainty in $$\sigma _{\text {tot}}$$ σ tot is dominated by the luminosity measurement, and in $$\rho $$ ρ by imperfect knowledge of the detector alignment and by modelling of the nuclear amplitude.