30,825 research outputs found
Diffuse PeV neutrinos from gamma-ray bursts
The IceCube collaboration recently reported the potential detection of two
cascade neutrino events in the energy range 1-10 PeV. We study the possibility
that these PeV neutrinos are produced by gamma-ray bursts (GRBs), paying
special attention to the contribution by untriggered GRBs that elude detection
due to their low photon flux. Based on the luminosity function, rate
distribution with redshift and spectral properties of GRBs, we generate, using
Monte-Carlo simulation, a GRB sample that reproduce the observed fluence
distribution of Fermi/GBM GRBs and an accompanying sample of untriggered GRBs
simultaneously. The neutrino flux of every individual GRBs is calculated in the
standard internal shock scenario, so that the accumulative flux of the whole
samples can be obtained. We find that the neutrino flux in PeV energies
produced by untriggered GRBs is about 2 times higher than that produced by the
triggered ones. Considering the existing IceCube limit on the neutrino flux of
triggered GRBs, we find that the total flux of triggered and untriggered GRBs
can reach at most a level of ~10^-9 GeV cm^-2 s^-1 sr^-1, which is insufficient
to account for the reported two PeV neutrinos. Possible contributions to
diffuse neutrinos by low-luminosity GRBs and the earliest population of GRBs
are also discussed.Comment: Accepted by ApJ, one more figure added to show the contribution to
the diffuse neutrino flux by untriggered GRBs with different luminosity,
results and conclusions unchange
Large-N scaling behavior of the quantum fisher information in the Dicke model
Quantum Fisher information (QFI) of the reduced two-atom state is employed to
capture the quantum criticality of the superradiant phase transition in the
Dicke model in the infinite size and finite- systems respectively. The
analytical expression of the QFI of its ground state is evaluated explicitly.
And finite-size scaling analysis is performed with the large accessible system
size due to the effective bosonic coherent-state technique. We also investigate
the large-size scaling behavior of the scaled QFI of the reduced -atom state
and show the accurate exponent.Comment: 6pages,2figure
DA-RNN: Semantic Mapping with Data Associated Recurrent Neural Networks
3D scene understanding is important for robots to interact with the 3D world
in a meaningful way. Most previous works on 3D scene understanding focus on
recognizing geometrical or semantic properties of the scene independently. In
this work, we introduce Data Associated Recurrent Neural Networks (DA-RNNs), a
novel framework for joint 3D scene mapping and semantic labeling. DA-RNNs use a
new recurrent neural network architecture for semantic labeling on RGB-D
videos. The output of the network is integrated with mapping techniques such as
KinectFusion in order to inject semantic information into the reconstructed 3D
scene. Experiments conducted on a real world dataset and a synthetic dataset
with RGB-D videos demonstrate the ability of our method in semantic 3D scene
mapping.Comment: Published in RSS 201
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