Pair Production of MSSM Higgs Bosons in the Non-decoupling Region at the LHC

We consider the Higgs boson signals from pair production at the LHC within the framework of the MSSM in the non-decoupling (low-m_A) region. In light of the recent observation of a SM-like Higgs boson, we argue that the exploration for Higgs pair production at the LHC is a crucial next step to probe the MSSM Higgs sector. We emphasize that the production of H^\pm A^0 and H^{+}H^{-} depends only on the electroweak gauge couplings while all the leading Higgs production channels via gluon fusion, vector-boson fusion, and Higgsstrahlung depend on additional free Higgs sector parameters. In the non-decoupling region, the five MSSM Higgs bosons are all relatively light and pair production signals may be accessible. We find that at the 8 TeV LHC, a 5\sigma signal for H^\pm A^0, H^\pm h^0 -> \tau^{\pm}\nu b\bar b and H^{+}H^{-} -> \tau^{+}\nu \tau^{-}\nu are achievable with an integrated luminosity of 7 (11) fb^{-1} and 24 (48) fb^{-1}, respectively for m_A=95 (130) GeV. At the 14 TeV LHC, a 5\sigma signal for these two channels would require as little as 4 (7) fb^{-1} and 10 (19) fb^{-1}, respectively.Comment: 20 pages, 8 figures and 3 tables. Version to appear in PR

Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies

Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation