In this work, we investigate the prospects of future $e^{+}{e}^{-}$ colliders in testing a Higgs triplet model with a scalar triplet and a scalar singlet under $SU(2)$. The parameters of the model are fixed so that the lightest $CP$-even state corresponds to the Higgs particle observed at the LHC at around 125 GeV. This study investigates if the second heaviest $CP$-even, the heaviest $CP$-odd and the singly charged states can be observed at existing and future colliders by computing their accessible production and decay channels. In general, the LHC is not well equipped to produce a Higgs boson which is not mainly doubletlike, so we turn our focus to lepton colliders. We find distinctive features of this model in cases where the second heaviest $CP$-even Higgs is tripletlike, singletlike or a mixture. These features could distinguish the model from other scenarios at future $e^{+}{e}^{-}$ colliders.

This paper explores the spatial variations of the statistical scaling features of low to high latitude geomagnetic field fluctuations at Swarm altitude. The data for this study comes from the vector field magnetometer onboard Swarm A satellite, measured at low resolution (1 Hz) for one year (from 9 March 2016, to 9 March 2017). We estimated the structure-function scaling exponents using the p-leaders discrete wavelet multifractal technique, from which we obtained the singularity spectrum related to the magnetic fluctuations in the North-East-Center (NEC) coordinate system. From this estimation, we retain just the maximal fractal subset, associated with the Hurst exponent H. Here we present thresholding for two levels of the Auroral Electrojet index and almost the whole northern and southern hemispheres, the Hurst exponent, the structure-function scaling exponent of order 2, and the multifractal p-exponent width for the geomagnetic fluctuations. The latter quantifies the relevance of the multifractal property. Sometimes, we found negative values of H, suggesting a behavior similar to wave breaking or shocklet-like propagating front. Furthermore, we found some asymmetries in the magnetic field turbulence between the northern and southern hemispheres. These estimations suggest that different turbulent regimes of the geomagnetic field fluctuations exist along the Swarm path.

We propose a method, based on Neural Networks, that detects the nonlinear robust interplanetary solar wind variables, with varying delays, driving the coupled behavior of three geomagnetic indices (Dst, AL, and AU). As opposed to minimizing a prediction error, the method is based on degrading the prediction by distorting the inputs of the trained Neural Networks in order to highlight the most sensible drivers. We show that the z component of the magnetic field, the duskward oriented electric field, and the speed of the particles of the interplanetary medium, at particular time delays, seem to be the most efficient drivers of the three coupled geomagnetic indices. Using only the sensible or robust drivers in the model, we demonstrate that iterated predictions during geomagnetic storm are significantly improved from models that only use one of the outstanding drivers with multiple time delays. The derived robust nonlinear Neural Network model is also a significant improvement over linear approximations, specially when used as iterated predictors.

This paper presents a novel method for mapping spectral features of the Moon using machine learning-based clustering of hyperspectral data from the Moon Mineral Mapper (M3) imaging spectrometer. The method uses a convolutional variational autoencoder to reduce the dimensionality of the spectral data and extract features of the spectra. Then, a k-means algorithm is applied to cluster the latent variables into five distinct groups, corresponding to dominant spectral features, which are related to the mineral composition of the Moon's surface. The resulting global spectral cluster map shows the distribution of the five clusters on the Moon, which consist of a mixture of, among others, plagioclase, pyroxene, olivine, and Fe-bearing minerals across the Moon's surface. The clusters are compared to the mineral maps from the Kaguya mission, which showed that the locations of the clusters overlap with the locations of high wt% of minerals such as plagioclase, clinopyroxene, and olivine. The paper demonstrates the usefulness of unbiased unsupervised learning for lunar mineral exploration and provides a comprehensive analysis of lunar mineralogy.

With the exponential growth of LHC (Large Hadron Collider) data in the years 2010-2012, distributed computing has become the established way to analyse collider data. The ATLAS experiment Grid infrastructure includes more than 130 sites worldwide, ranging from large national computing centres to smaller university clusters. So far the storage technologies and access protocols to the clusters that host this tremendous amount of data vary from site to site. HTTP/WebDAV offers the possibility to use a unified industry standard to access the storage. We present the deployment and testing of HTTP/WebDAV for local and remote data access in the ATLAS experiment for the new data management system Rucio and the PanDA workload management system. Deployment and large scale tests have been performed using the Grid testing system HammerCloud and the ROOT HTTP plugin Davix.