1,259 research outputs found
Excited neutrino search potential of the FCC-based electron-hadron colliders
The production potential of the excited neutrinos at the FCC-based
electron-hadron colliders, namely the ERL60FCC with
TeV, the ILCFCC with TeV, and the PWFA-LCFCC
with TeV, has been analyzed. The branching ratios of the
excited neutrinos have been calculated for the different decay channels and
shown that the dominant channel is . We have
calculated the production cross sections with the process of
and the decay widths of the
excited neutrinos with the process of . The
signals and corresponding backgrounds are studied in detail to obtain
accessible mass limits. It is shown that the discovery limits obtained on the
mass of the excited neutrino are GeV for ,
GeV for ( GeV for ), and
GeV for ( GeV for ),
for the center-of-mass energies of , , and TeV, respectively.Comment: 16 pages, 9 figures, 5 table
Excited muon searches at the FCC based muon-hadron colliders
We study the excited muon production at the FCC based muon-hadron colliders.
We give the excited muon decay widths and production cross section. We deal
with the process and we
plot the transverse momentum, rapidity and invariant mass distributions of
final state particles to get the discovery cuts. By using discovery cuts, we
get the mass limits for excited muons. It is shown that the discovery limits on
the mass of are 2.2 TeV, 5.9 TeV and 7.5 TeV for -FCC,
-FCC and -FCC, respectively.Comment: 13 pages, 10 figures, 3 tables, version of published in Adv. High
Energy Physic
Single production of composite electrons at the future SPPC-based lepton-hadron colliders
We consider the production of excited electrons with spin-1/2 at the future
SPPC-based electron-proton colliders with center-of-mass energies of ,
, and TeV. These exotic particles are predicted in the
composite models. We calculate the production cross-sections and concentrate on
the photon decay channel of the excited electrons with the process of
. The pseudorapidity and
transverse momentum distributions of the electrons and photons in the
final-state have been plotted in order to determine the kinematical cuts best
suited for discovery of the excited electrons. By applying these cuts we
compute , and contour plots of the statistical
significance of the expected signal in the parameter space (, ),
where denotes the integrated luminosity of the collider and is
the mass of the composite electrons.Comment: 21 pages, 9 figures, 6 table
The Lie Brackets on Time Scales
The Lie derivative, which has a wide range of application in physics and geometry, is trying to be examined on time scales. Firstly, nabla Lie bracket is defined on two-dimensional time scales. Secondly, the nabla Lie multiplication and some properties are given on the time scales. Lastly, for analyzing the differences between the real Lie multiplication and the nabla Lie multiplication, a numerical example is given
The archipelago of press restriction in Turkey
Turkey’s independent media died a slow and painful death, a result of years of co-option, censorship and repression. But critical journalism faded with a whimper and not a bang even before Erdogan and the Justice and Development Party (AKP) came to power
Right for the Right Reason: Training Agnostic Networks
We consider the problem of a neural network being requested to classify
images (or other inputs) without making implicit use of a "protected concept",
that is a concept that should not play any role in the decision of the network.
Typically these concepts include information such as gender or race, or other
contextual information such as image backgrounds that might be implicitly
reflected in unknown correlations with other variables, making it insufficient
to simply remove them from the input features. In other words, making accurate
predictions is not good enough if those predictions rely on information that
should not be used: predictive performance is not the only important metric for
learning systems. We apply a method developed in the context of domain
adaptation to address this problem of "being right for the right reason", where
we request a classifier to make a decision in a way that is entirely 'agnostic'
to a given protected concept (e.g. gender, race, background etc.), even if this
could be implicitly reflected in other attributes via unknown correlations.
After defining the concept of an 'agnostic model', we demonstrate how the
Domain-Adversarial Neural Network can remove unwanted information from a model
using a gradient reversal layer.Comment: Author's original versio
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