956 research outputs found
Twist-3 contribution to the pion electromagnetic form factor
Non-leading contribution to the pion electromagnetic form factor which comes
from the pion twist-3 wave function is analyzed in the modified hard scattering
approach (MHSA) proposed by Li and Sterman. This contribution is enhanced
significantly due to bound state effect (the twist-3 wave function is
independent of the fractional momentum carried by the parton and has a large
factor with being the pion meson mass and
being the mean u- and d-quark masses). Consequently, although it is suppressed
by the factor , the twist-3 contribution is comparable with and even
larger than the leading twist (twist-2) contribution at intermediate energy
region of being .Comment: 10 pages, 2 fgures, latex. More discussions on the Sudakov effect
added, references added. To appear in European Physical Journal C
(Zeitschrift fur Physik C
Determination of impact parameter in high-energy heavy-ion collisions via deep learning
In this study, Au+Au collisions with the impact parameter of fm at GeV are simulated by the AMPT model to
provide the preliminary final-state information. After transforming these
information into appropriate input data (the energy spectra of final-state
charged hadrons), we construct a deep neural network (DNN) and a convolutional
neural network (CNN) to connect final-state observables with impact parameters.
The results show that both the DNN and CNN can reconstruct the impact
parameters with a mean absolute error about fm with CNN behaving slightly
better. Then, we test the neural networks for different beam energies and
pseudorapidity ranges in this task. It turns out that these two models work
well for both low and high energies. But when making test for a larger
pseudorapidity window, we observe that the CNN shows higher prediction accuracy
than the DNN. With the method of Grad-CAM, we shed light on the `attention'
mechanism of the CNN model
Detecting Chiral Magnetic Effect via Deep Learning
The search of chiral magnetic effect (CME) in heavy-ion collisions has
attracted long-term attentions. Multiple observables have been proposed but all
suffer from obstacles due to large background contaminations. In this Letter,
we construct an observable-independent CME-meter based on a deep convolutional
neural network. After trained over data set generated by a multiphase transport
model, the CME-meter shows high accuracy in recognizing the CME-featured charge
separation from the final-state pion spectra. It also exhibits remarkable
robustness to diverse conditions including different collision energies,
centralities, and elliptic flow backgrounds. In a transfer learning manner, the
CME-meter is validated in isobaric collision systems, showing good
transferability among different colliding systems. Based on variational
approaches, we utilize the DeepDream method to derive the most responsive
CME-spectra that demonstrates the physical contents the machine learns.Comment: 7 pages, 10 figure
Natural Counterfactuals With Necessary Backtracking
Counterfactual reasoning is pivotal in human cognition and especially
important for providing explanations and making decisions. While Judea Pearl's
influential approach is theoretically elegant, its generation of a
counterfactual scenario often requires interventions that are too detached from
the real scenarios to be feasible. In response, we propose a framework of
natural counterfactuals and a method for generating counterfactuals that are
natural with respect to the actual world's data distribution. Our methodology
refines counterfactual reasoning, allowing changes in causally preceding
variables to minimize deviations from realistic scenarios. To generate natural
counterfactuals, we introduce an innovative optimization framework that permits
but controls the extent of backtracking with a naturalness criterion. Empirical
experiments indicate the effectiveness of our method
Deep Learning of Phase Transitions for Quantum Spin Chains from Correlation Aspects
Using machine learning (ML) to recognize different phases of matter and to
infer the entire phase diagram has proven to be an effective tool given a large
dataset. In our previous proposals, we have successfully explored phase
transitions for topological phases of matter at low dimensions either in a
supervised or an unsupervised learning protocol with the assistance of quantum
information related quantities. In this work, we adopt our previous ML
procedures to study quantum phase transitions of magnetism systems such as the
XY and XXZ spin chains by using spin-spin correlation functions as the input
data. We find that our proposed approach not only maps out the phase diagrams
with accurate phase boundaries, but also indicates some new features that have
not observed before. In particular, we define so-called relevant correlation
functions to some corresponding phases that can always distinguish between
those and their neighbors. Based on the unsupervised learning protocol we
proposed [Phys. Rev. B 104, 165108 (2021)], the reduced latent representations
of the inputs combined with the clustering algorithm show the connectedness or
disconnectedness between neighboring clusters (phases), just corresponding to
the continuous or disrupt quantum phase transition, respectively.Comment: 18 pages, 21 figure
Tumor Necrosis Factor- α
Neonatal sepsis (NS) is an important cause of mortality in newborns and life-threatening disorder in infants. The meta-analysis was performed to investigate the diagnosis value of tumor necrosis factor-α (TNF-α) test in NS. Our collectible studies were searched from PUBMED, EMBASE, and the Cochrane Library between March 1994 and August 2013. Accordingly, 347 studies were collected totally, in which 15 articles and 23 trials were selected to study the NS in our meta-analysis. The TNF-α test showed moderate accuracy of the diagnosis of NS both in early-onset neonatal sepsis (sensitivity = 0.66, specificity = 0.76, Q* = 0.74) and in late-onset neonatal sepsis (sensitivity = 0.68, specificity = 0.89, Q* = 0.87). We also found the northern hemisphere group in the test has higher sensitivity (0.84) and specificity (0.83). A diagnostic OR analysis found that the study population may be the major reason for the heterogeneity. Accordingly, we suggest that TNF-α is also a valuable marker in the diagnosis of NS
Poynting vector, energy density and energy velocity in anomalous dispersion medium
The Poynting vector, energy density and energy velocity of light pulses
propagating in anomalous dispersion medium (used in WKD-like experiments) are
calculated. Results show that a negative energy density in the medium
propagates along opposite of incident direction with such a velocity similar to
the negative group velocity while the direction of the Poynting vector is
positive. In other words, one might say that a positive energy density in the
medium would propagate along the positive direction with a speed having
approximately the absolute valueof the group velocity. We further point out
that neither energy velocity nor group velocity is a good concept to describe
the propagation process of light pulse inside the medium in WKD experiment
owing to the strong accumulation and dissipation effects.Comment: 6 page
- …