70 research outputs found
Energy Flow in the Hadronic Final State of Diffractive and Non-Diffractive Deep-Inelastic Scattering at HERA
An investigation of the hadronic final state in diffractive and
non--diffractive deep--inelastic electron--proton scattering at HERA is
presented, where diffractive data are selected experimentally by demanding a
large gap in pseudo --rapidity around the proton remnant direction. The
transverse energy flow in the hadronic final state is evaluated using a set of
estimators which quantify topological properties. Using available Monte Carlo
QCD calculations, it is demonstrated that the final state in diffractive DIS
exhibits the features expected if the interaction is interpreted as the
scattering of an electron off a current quark with associated effects of
perturbative QCD. A model in which deep--inelastic diffraction is taken to be
the exchange of a pomeron with partonic structure is found to reproduce the
measurements well. Models for deep--inelastic scattering, in which a
sizeable diffractive contribution is present because of non--perturbative
effects in the production of the hadronic final state, reproduce the general
tendencies of the data but in all give a worse description.Comment: 22 pages, latex, 6 Figures appended as uuencoded fil
A Search for Selectrons and Squarks at HERA
Data from electron-proton collisions at a center-of-mass energy of 300 GeV
are used for a search for selectrons and squarks within the framework of the
minimal supersymmetric model. The decays of selectrons and squarks into the
lightest supersymmetric particle lead to final states with an electron and
hadrons accompanied by large missing energy and transverse momentum. No signal
is found and new bounds on the existence of these particles are derived. At 95%
confidence level the excluded region extends to 65 GeV for selectron and squark
masses, and to 40 GeV for the mass of the lightest supersymmetric particle.Comment: 13 pages, latex, 6 Figure
A Measurement of the Proton Structure Function
A measurement of the proton structure function is reported
for momentum transfer squared between 4.5 and 1600 and
for Bjorken between and 0.13 using data collected by the
HERA experiment H1 in 1993. It is observed that increases
significantly with decreasing , confirming our previous measurement made
with one tenth of the data available in this analysis. The dependence is
approximately logarithmic over the full kinematic range covered. The subsample
of deep inelastic events with a large pseudo-rapidity gap in the hadronic
energy flow close to the proton remnant is used to measure the "diffractive"
contribution to .Comment: 32 pages, ps, appended as compressed, uuencoded fil
Experimental access to Transition Distribution Amplitudes with the P̄ANDA experiment at FAIR
Diverse deep neural networks all predict human inferior temporal cortex well, after training and fitting
Deep neural networks (DNNs) trained on object recognition provide the best current models of high-level visual cortex. What remains unclear is how strongly experimental choices, such as network architecture, training, and fitting to brain data, contribute to the observed similarities. Here, we compare a diverse set of nine DNN architectures on their ability to explain the representational geometry of 62 object images in human inferior temporal cortex (hIT), as measured with fMRI. We compare untrained networks to their task-trained counterparts and assess the effect of cross-validated fitting to hIT, by taking a weighted combination of the principal components of features within each layer and, subsequently, a weighted combination of layers. For each combination of training and fitting, we test all models for their correlation with the hIT representational dissimilarity matrix, using independent images and subjects. Trained models outperform untrained models (accounting for 57% more of the explainable variance), suggesting that structured visual features are important for explaining hIT. Model fitting further improves the alignment of DNN and hIT representations (by 124%), suggesting that the relative prevalence of different features in hIT does not readily emerge from the Imagenet object-recognition task used to train the networks. The same models can also explain the disparate representations in primary visual cortex (V1), where stronger weights are given to earlier layers. In each region, all architectures achieved equivalently high performance once trained and fitted. The models' shared properties—deep feedforward hierarchies of spatially restricted nonlinear filters - seem more important than their differences, when modeling human visual representations
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