The study of Higgs decays to bottom quarks and tau leptons. Determination of theĬouplings to down-type fermions requires direct measurement of theĬorresponding Higgs boson decays, as recently reported by the CMS experiment in Particular to down-type fermions, since the current measurements mainlyĬonstrain the couplings to the up-type top quark. Open question is whether the new particle also couples to fermions, and in Long-sought agent responsible for electroweak symmetry breaking. Revealed that the properties of the new boson are consistent with those of the Pairs, an extensive set of measurements of the mass and couplings to W and Zīosons, as well as multiple tests of the spin-parity quantum numbers, have Since the first observation in decays to gamma gamma, WW, and ZZ boson Symmetry breaking and possibly completing the standard model of particle The LHC has heralded a new era in understanding the nature of electroweak The discovery of a new boson with a mass of approximately 125 GeV in 2012 at Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. Exclusion regions are also provided in the parameter space of the habemus Minimal Supersymmetric Standard Model and the Electroweak Singlet Model.ĭeep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. In addition, limits are set on the production of narrow scalar resonances and spin-2 Kaluza–Klein Randall–Sundrum gravitons. This letter presents a combination of searches for Higgs boson pair production using up to 36.1 fb^<12.0). We also discuss the integration of the results into the public code FeynHiggs. It is shown that within this scheme a recently proposed resummation of large gluino contributions is absorbed into the model parameters, resulting in reliable and numerically stable predictions in the heavy-gluino region. We demonstrate that the theoretical predictions in the heavy gluino region are vastly improved by the introduction of a suitable renormalisation scheme for the EFT calculation. For the phenomenologically interesting case of a significant hierarchy between the gluino mass and the masses of the scalar top quarks the predictions suffer from large theoretical uncertainties related to non-decoupling power-enhanced gluino contributions in the EFT results employing the DR‾ renormalisation scheme. State-of-the-art predictions for the mass of the lightest MSSM Higgs boson usually involve the resummation of higher-order logarithmic contributions obtained within an effective-field-theory (EFT) approach, often combined with a fixed-order calculation into a hybrid result.
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