20,520 research outputs found
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
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