349 research outputs found
Photon tagged correlations in heavy ion collisions
A detailed study of various two-particle correlation functions involving
photons and neutral pions is presented in proton-proton and lead-lead
collisions at the LHC energy. The aim is to use these correlation functions to
quantify the effect of the medium (in lead-lead collisions) on the jet decay
properties. The calculations are carried out at the leading order in QCD but
the next-to-leading order corrections are also discussed. The competition
between different production mechanisms makes the connection between the jet
energy loss spectrum and the gamma-pi correlations somewhat indirect while the
gamma-gamma correlations have a clearer relation to the jet fragmentation
properties.Comment: 32 pages, 19 figures. Minor changes, published versio
Deciphering the properties of the medium produced in heavy ion collisions at RHIC by a pQCD analysis of quenched large spectra
We discuss the question of the relevance of perturbative QCD calculations for
analyzing the properties of the dense medium produced in heavy ion collisions.
Up to now leading order perturbative estimates have been worked out and
confronted with data for quenched large hadron spectra. Some of
them are giving paradoxical results, contradicting the perturbative framework
and leading to speculations such as the formation of a strongly interacting
quark-gluon plasma. Trying to bypass some drawbacks of these leading order
analysis and without performing detailed numerical investigations, we collect
evidence in favour of a consistent description of quenching and of the
characteristics of the produced medium within the pQCD framework.Comment: 10 pages, 3 figure
System size dependence of nuclear modification and azimuthal anisotropy of jet quenching
We investigate the system size dependence of jet-quenching by analyzing
transverse momentum spectra of neutral pions in Au+Au and Cu+Cu collisions at
=200 GeV for different centralities. The fast partons
are assumed to lose energy by radiating gluons as they traverse the plasma and
undergo multiple collisions. The energy loss per collision, , is
taken as proportional to (where is the energy of the parton),
proportional to , or a constant depending on whether the formation
time of the gluon is less than the mean path, greater than the mean free path
but less than the path length, or greater than the path length of the partons,
respectively. NLO pQCD is used to evaluate pion production by modifying the
fragmentation function to account for the energy loss. We reproduce the nuclear
modification factor by treating as the only free
parameter, depending on the centrality and the mechanism of energy loss. These
values are seen to explain the nuclear modification of prompt photons, caused
by the energy lost by final state quarks before they fragment into photons.
These also reproduce the azimuthal asymmetry of transverse momentum
distribution for pions within a factor of two and for prompt photons in a fair
agreement with experimental data.Comment: 26 pages, 17 figures. One more figure added. Discussion expanded.
Typographical corrections done, several references added. To appear in
Journal of Physics
Atomic Mass Dependence of Hadron Production in Deep Inelastic Scattering on Nuclei
Hadron production in lepton-nucleus deep inelastic scattering is studied in
an absorption model. In the proposed model, the early stage of hadronization in
the nuclear medium is dominated by prehadron formation and absorption,
controlled by flavor-dependent formation lengths and absorption cross sections.
Computations for hadron multiplicity ratios are presented and compared with the
HERMES experimental data for pions, kaons, protons and antiprotons. The
mass-number dependence of hadron attenuation is shown to be sensitive to the
underlying hadronization dynamics. Contrary to common expectations for
absorption models, a leading term proportional to A^{2/3} is found. Deviations
from the leading behavior arise at large mass-numbers and large hadron
fractional momenta.Comment: 30 pages, 10 figures, v2: minor changes (legend in figs 5 & 6 is
added), v3: additional explanations are added, v4: Version combines v3 and
the erratum hep-ph/050803
Spatial orientation in navigating agents: Modeling head-direction cells
A model that is consistent with several neurophysiological properties of biological head-direction cells is presented. The dynamics of the system is primarily controlled by idiothetic signals which determine the direction selectivity property. By means of LTP correlation learning, allothetic cues are incorporated to stabilize the direction representation over time. The interaction between allothetic and idiothetic signals to control head-direction cells is studied. Experimental results obtained by validating the model on a mobile Khepera robot are given. The neural system enables the robot to track its allocentric heading effectively
Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity
A computational model of hippocampal activity during spatial cognition and navigation tasks is presented. The spatial representation in our model of the rat hippocampus is built on-line during exploration via two processing streams. An allothetic vision-based representation is built by unsupervised Hebbian learning extracting spatio-temporal properties of the environment from visual input. An idiothetic representation is learned based on internal movement-related information provided by path integration. On the level of the hippocampus, allothetic and idiothetic representations are integrated to yield a stable representation of the environment by a population of localized overlapping CA3-CA1 place fields. The hippocampal spatial representation is used as a basis for goal-oriented spatial behavior. We focus on the neural pathway connecting the hippocampus to the nucleus accumbens. Place cells drive a population of locomotor action neurons in the nucleus accumbens. Reward-based learning is applied to map place cell activity into action cell activity. The ensemble action cell activity provides navigational maps to support spatial behavior. We present experimental results obtained with a mobile Khepera robot
Charmonium suppression in p-A collisions at RHIC
We discuss charmonium production in proton-nucleus collisions at RHIC
energies under the assumption of xF and x2 scaling. We find that all the
ambiguities due to energy loss are gone at this energy and therefore data will
reveal the scaling law, if any. These p-A data will also be crucial to
interpret nucleus-nucleus data with respect to a possible formation of a quark
gluon plasma because the extrapolations for charmonium production from the
present p-A data to RHIC energies, based on the two scaling laws, differ by a
factor of four.Comment: 6 pages, 3 figures. New section on shadowing and energy loss,
References adde
Cold nuclear matter effects on J/psi production: intrinsic and extrinsic transverse momentum effects
Cold nuclear matter effects on J/psi production in proton-nucleus and
nucleus-nucleus collisions are evaluated taking into account the specific J/psi
production kinematics at the partonic level, the shadowing of the initial
parton distributions and the absorption in the nuclear matter. We consider two
different parton processes for the c-cbar pair production: one with collinear
gluons and a recoiling gluon in the final state and the other with initial
gluons carrying intrinsic transverse momentum. Our results are compared to RHIC
observables. The smaller values of the nuclear modification factor R_AA in the
forward rapidity region (with respect to the mid rapidity region) are partially
explained, therefore potentially reducing the need for recombination effects.Comment: 7 pages, 11 figures, LaTeX, uses elsarticle.cls (included).v2:
version (with minor text revisions and Fig 2 and 4a modified) to appear in
Phys.Lett.
Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning
We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot
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