510 research outputs found
Discovery reach for wino and higgsino dark matter with a disappearing track signature at a 100 TeV collider
Within the theory of supersymmetry, the lightest neutralino is a dark matter
candidate and is often assumed to be the lightest supersymmetric particle (LSP)
as well. If the neutral wino or higgsino is dark matter, the upper limit of the
LSP mass is determined by the observed relic density of dark matter. If the LSP
is a nearly-pure neutral state of the wino or higgsino, the lightest chargino
state is expected to have a significant lifetime due to a tiny mass difference
between the LSP and the chargino. This article presents discovery potential of
the 100 TeV future circular hadron collider (FCC) for the wino and higgsino
dark matter using a disappearing-track signature. The search strategy to extend
the discovery reach to the thermal limits of wino/higgsino dark matter is
discussed with detailed studies on the background rate and the reference design
of the FCC-hadron detector under possible running scenarios of the FCC-hadron
machine. A proposal of modifying the detector layout and several ideas to
improve the sensitivity further are also discussed.Comment: 10 pages, 7 figures, 4 table
Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning
Super-resolution is a machine-learning technique in image processing which
generates high-resolution images from low-resolution images. Inspired by this
approach, we perform a numerical experiment of quantum machine learning, which
takes low-resolution (low plane-wave energy cutoff) one-particle molecular
wavefunctions in plane-wave basis as input and generates high-resolution (high
plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems,
and study the performance of different learning models. We show that the
trained models can generate wavefunctions having higher fidelity values with
respect to the ground-truth wavefunctions than a simple linear interpolation,
and the results can be improved both qualitatively and quantitatively by
including data-dependent information in the ansatz. On the other hand, the
accuracy of the current approach deteriorates for wavefunctions calculated in
electronic configurations not included in the training dataset. We also discuss
the generalization of this approach to many-body electron wavefunctions.Comment: 13 pages, 18 figure
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