Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.

Abstract: The electromagnetic coupling axion-photon in a microwave cavity is revisited with the Boundary Integral-Resonant Mode Expansion (BI-RME) 3D technique. Such full-wave modal technique has been applied for the rigorous analysis of the excitation of a microwave cavity with an axion field. In this scenario, the electromagnetic field generated by the axion-photon coupling can be assumed to be driven by equivalent electrical charge and current densities. These densities have been inserted in the general BI-RME 3D equations, which express the RF electromagnetic field existing within a cavity as an integral involving the Dyadic Green's functions of the cavity (under Coulomb gauge) as well as such densities. This method is able to take into account any arbitrary spatial and temporal variation of both magnitude and phase of the axion field. Next, we have obtained a simple network driven by the axion current source, which represents the coupling between the axion field and the resonant modes of the cavity. With this approach, it is possible to calculate the extracted and dissipated RF power as a function of frequency along a broad band and without Cauchy-Lorentz approximations, obtaining the spectrum of the electromagnetic field generated in the cavity, and dealing with modes relatively close to the axion resonant mode. Moreover, with this technique we have a complete knowledge of the signal extracted from the cavity, not only in magnitude but also in phase. This can be an interesting issue for future analysis where the axion phase is an important parameter.

Abstract: Non-ionizing energy loss causes bulk damage to the silicon sensors of the ATLAS pixel and strip detectors. This damage has important implications for data-taking operations, charged-particle track reconstruction, detector simulations, and physics analysis. This paper presents simulations and measurements of the leakage current in the ATLAS pixel detector and semiconductor tracker as a function of location in the detector and time, using data collected in Run 1 (2010-2012) and Run 2 (2015-2018) of the Large Hadron Collider. The extracted fluence shows a much stronger vertical bar z vertical bar-dependence in the innermost layers than is seen in simulation. Furthermore, the overall fluence on the second innermost layer is significantly higher than in simulation, with better agreement in layers at higher radii. These measurements are important for validating the simulation models and can be used in part to justify safety factors for future detector designs and interventions.

Abstract: Primordial black holes (PBHs) are a potential dark matter candidate whose masses can span over many orders of magnitude. If they have masses in the 10(15)-10(17) g range, they can emit sizeable fluxes of MeV neutrinos through evaporation via Hawking radiation. We explore the possibility of detecting light (non-)rotating PBHs with future neutrino experiments. We focus on two next generation facilities: the Deep Underground Neutrino Experiment (DUNE) and THEIA. We simulate the expected event spectra at both experiments assuming different PBH mass distributions and spins, and we extract the expected 95% C.L. sensitivities to these scenarios. Our analysis shows that future neutrino experiments like DUNE and THEIA will be able to set competitive constraints on PBH dark matter, thus providing complementary probes in a part of the PBH parameter space currently constrained mainly by photon data.

Abstract: Heavy neutral leptons (HNLs) with masses around the electroweak scale are expected to be rather long-lived particles, as a result of the observed smallness of the active neutrino masses. In this work, we study long-lived HNLs in NRSMEFT, a Standard Model (SM) extension with singlet fermions to which we add non-renormalizable operators up to dimension-6. Operators which contain two HNLs can lead to a sizable enhancement of the production cross sections, compared to the minimal case where HNLs are produced only via their mixing with the SM neutrinos. We calculate the expected sensitivities for the ATLAS detector and the future far-detector experiments: AL3X, ANUBIS, CODEX-b, FASER, MATHUSLA, and MoEDAL-MAPP in this setup. The sensitive ranges of the HNL mass and of the active-heavy mixing angle are much larger than those in the minimal case. We study both, Dirac and Majorana, HNLs and discuss how the two cases actually differ phenomenologically, for HNL masses above roughly 100 GeV.