10,015 research outputs found
Limitations on Dimensional Regularization in Renyi Entropy
Dimensional regularization is a common method used to regulate the UV
divergence of field theoretic quantities. When it is used in the context of
Renyi entropy, however, it is important to consider whether such a procedure
eliminates the statistical interpretation thereof as a measure of entanglement
of states living on a Hilbert space. We therefore examine the dimensionally
regularized Renyi entropy of a 4d unitary CFT and show that it admits no
underlying Hilbert space in the state-counting sense. This gives a concrete
proof that dimensionally regularized Renyi entropy cannot always be obtained as
a limit of the Renyi entropy of some finite-dimensional quantum system.Comment: 10 pages; v2: Minor corrections of typos; v3: Small modification of
conclusion sectio
LHC Signatures of Two-Higgs-Doublets with Fourth Family
On-going Higgs searches in the light mass window are of vital importance for
testing the Higgs mechanism and probing new physics beyond the standard model
(SM). The latest ATLAS and CMS searches for the SM Higgs boson at the LHC
(7TeV) found some intriguing excesses of events in the \gamma\gamma/VV^*
channels (V=Z,W) around the mass-range of 124-126 GeV. We explore a possible
explanation of the \gamma\gamma and VV^* signals from the light CP-odd Higgs
A^0 or CP-even Higgs h^0 from the general two-Higgs-doublet model with
fourth-family fermions. We demonstrate that by including invisible decays of
the Higgs boson A^0 or h^0 to fourth-family neutrinos, the predicted
\gamma\gamma and VV^* signals can explain the observed new signatures at the
LHC, and will be further probed by the forthcoming LHC runs in 2012.Comment: 22pp, 10 Figs, JHEP published version, references adde
Statistical computation of Boltzmann entropy and estimation of the optimal probability density function from statistical sample
In this work, we investigate the statistical computation of the Boltzmann
entropy of statistical samples. For this purpose, we use both histogram and
kernel function to estimate the probability density function of statistical
samples. We find that, due to coarse-graining, the entropy is a monotonic
increasing function of the bin width for histogram or bandwidth for kernel
estimation, which seems to be difficult to select an optimal bin
width/bandwidth for computing the entropy. Fortunately, we notice that there
exists a minimum of the first derivative of entropy for both histogram and
kernel estimation, and this minimum point of the first derivative
asymptotically points to the optimal bin width or bandwidth. We have verified
these findings by large amounts of numerical experiments. Hence, we suggest
that the minimum of the first derivative of entropy be used as a selector for
the optimal bin width or bandwidth of density estimation. Moreover, the optimal
bandwidth selected by the minimum of the first derivative of entropy is purely
data-based, independent of the unknown underlying probability density
distribution, which is obviously superior to the existing estimators. Our
results are not restricted to one-dimensional, but can also be extended to
multivariate cases. It should be emphasized, however, that we do not provide a
robust mathematical proof of these findings, and we leave these issues with
those who are interested in them.Comment: 8 pages, 6 figures, MNRAS, in the pres
The feasibility of transferring clean technology from the United States to China : a case study from the paper industry
In their pursuit of economic development, many developing countries are causing pollution at an alarming rate. One solution to the problem is the use of clean technology. Some developed countries have created various manufacture technologies which result in pollution prevention. There is a need for the transfer of clean technology from the developed countries to developing countries. It is beneficial to study the feasibility of such transfer.
The manufacturing processes of the paper industry are selected for this study. An investigation and comparison was conducted in order to evaluate the feasibility of clean technology transfer from the United States to China. The study found that it is technically simple but socially complex to transfer clean technology from U.S. industry to the Chinese counterpart
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