Accelerator Neutrinos

Abstract:

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs

Abstract:

Abstract A search for a heavy neutral Higgs boson, A , decaying into a Z boson and another heavy Higgs boson, H , is performed using a data sample corresponding to an integrated luminosity of 139 fb $$^{-1}$$ -1 from proton–proton collisions at $$\sqrt{s} = 13$$ s=13 $$\text {TeV}$$ TeV recorded by the ATLAS detector at the LHC. The search considers the Z boson decaying into electrons or muons and the H boson into a pair of b -quarks or W bosons. The mass range considered is 230–800 $$\text {GeV}$$ GeV for the A boson and 130–700 $$\text {GeV}$$ GeV for the H boson. The data are in good agreement with the background predicted by the Standard Model, and therefore 95% confidence-level upper limits for $$\sigma \times B(A\rightarrow ZH)\times B(H\rightarrow bb \; \text {or} \; H \rightarrow WW)$$ σ×B(A→ZH)×B(H→bborH→WW) are set. The upper limits are in the range 0.0062–0.380 pb for the $$H\rightarrow bb$$ H→bb channel and in the range 0.023–8.9 pb for the $$H\rightarrow WW$$ H→WW channel. An interpretation of the results in the context of two-Higgs-doublet models is also given.