Explore the vast range of titles available.

The ESSnuSB experiment aims to measure the leptonic CP phase \\(\\delta_{CP}\\) with an unprecedented resolution by probing neutrino oscillations at the second oscillation maximum. In the present work, the complementarity between the long-baseline neutrino program and atmospheric neutrinos is investigated for ESSnuSB. By simulating atmospheric neutrino events equivalent of 5.4 Mt\\(\\cdot\\)year exposure, the resolution for \\(\\delta_{\\rm CP}^{}\\) is found to improve from \\(7.5^\\circ\\) (\\(6.7^\\circ\\)) to \\(7.1^\\circ\\) (\\(6.5^\\circ\\)) at \\(1\\sigma\\)~CL for \\(\\delta_{\\rm CP}^{} = -90^\\circ\\) (\\(+90^\\circ\\)) with respect to super-beam neutrinos, resolving also the degeneracies arising from neutrino mass ordering. These findings highlight the synergies that exist between super-beam neutrinos and atmospheric neutrinos in ESSnuSB.

Atmospheric neutrinos provide a unique avenue to study neutrino interactions in matter. In this work, the prospects of constraining non-standard neutrino interactions with atmospheric neutrino oscillations are investigated for the proposed ESSnuSB far detector. By analyzing atmospheric neutrino samples equivalent to 5.4 Mt\\(\\cdot\\)year exposure, it is found that ESSnuSB could be able to set the upper bounds \\(|\\epsilon_{e\\mu}^m| < 0.053, |\\epsilon_{e\\tau}^m| < 0.057, |\\epsilon_{\\mu\\tau}^m| < 0.021, \\epsilon_{ee}^m - \\epsilon_{\\mu\\mu}^m < 0.075\\) and \\(|\\epsilon_{\\tau\\tau}^m - \\epsilon_{\\mu\\mu}^m| < 0.031\\) at \\(90\\%\\) CL, when the results are minimized for \\(\\phi_{e\\mu}^m, \\phi_{e\\tau}^m\\) and \\(\\phi_{\\mu\\tau}^m\\) and normal ordering is assumed for neutrino masses. It is also shown that the presence of non-standard interactions could affect the sensitivities to neutrino mass ordering and \\(\\theta_{23}^{}\\) octant in comparison to the standard interaction scheme. The results of this work highlight the complementarity between atmospheric and accelerator neutrino programs in ESSnuSB.

Neutrino oscillations constitute an excellent tool to probe physics beyond the Standard Model. In this paper, we investigate the potential of the ESSnuSB experiment to constrain the effects of flavour-dependent long-range forces (LRFs) in neutrino oscillations, which may arise due to the extension of the Standard Model gauge group by introducing new \\(U(1)\\) symmetries. Focusing on three specific \\(U(1)\\) symmetries -- \\(L_e - L_\\), \\(L_e - L_\\), and \\(L_ - L_\\), we demonstrate that ESSnuSB offers a favourable environment to search for LRF effects. Our analyses reveal that ESSnuSB can set \\(90\\%\\) confidence level bounds of \\(V_e < 2.99 10^-14 \\, eV\\), \\(V_e < 2.05 10^-14 \\, eV\\), and \\(V_ < 1.81 10^-14 \\, eV\\), which are competitive to the upcoming Deep Underground Neutrino Experiment (DUNE). It is also observed that reducing the systematic uncertainties from \\(5\\%\\) to \\(2\\%\\) improves the ESSnuSB limits on \\(V_\\). Interestingly, we find limited correlations between LRF parameters and the less constrained lepton mixing parameters \\(_23\\) and \\(_CP\\), preserving the robustness of ESSnuSB's sensitivity to CP violation. Even under extreme LRF potentials (\\(V_ 10^-13 \\, eV\\)), the CP-violation sensitivity and \\(_CP\\) precision remain largely unaffected. These results establish ESSnuSB as a competitive experimental setup for probing LRF effects, complementing constraints from other neutrino sources and offering critical insights into the physics of long-range forces.

In the effort to obtain a precise measurement of leptonic CP-violation with the ESS\\(\\nu\\)SB experiment, accurate and fast reconstruction of detector events plays a pivotal role. In this work, we examine the possibility of replacing the currently proposed likelihood-based reconstruction method with an approach based on Graph Neural Networks (GNNs). As the likelihood-based reconstruction method is reasonably accurate but computationally expensive, one of the benefits of a Machine Learning (ML) based method is enabling fast event reconstruction in the detector development phase, allowing for easier investigation of the effects of changes to the detector design. Focusing on classification of flavour and interaction type in muon and electron events and muon- and electron neutrino interaction events, we demonstrate that the GNN reconstructs events with greater accuracy than the likelihood method for events with greater complexity, and with increased speed for all events. Additionally, we investigate the key factors impacting reconstruction performance, and demonstrate how separation of events by pion production using another GNN classifier can benefit flavour classification.