79230
Contributions of gluon recombination to saturation phenomena
Parton distributions in the small $x$ region are numerically predicted by using a modified DGLAP equation with the GRV-like input distributions. We find that gluon recombination at twist-4 level obviously suppresses the rapid growth of parton densities with $x$ decrease. We show that before the saturation scale $Q^2_s$ is reached, saturation and partial saturation appear in the small $x$ behavior of parton distributions in nucleus and free proton, respectively. The antishadowing contributions to the saturation phenomena are also discussed.
119716730
A twist-4 effect in the parton evolution processes
Recently, a research group reported that they can fit the F2 data well by using the Altarelli-Parisi evolution equation with a valence-like initial gluon distribution, which means that we need not consider the high twist effects in the proton. In this letter, we point out that their valence-like initial gluon distribution is unreasonable for the proton structure function. With the reasonable initial distributions, we find that the high twist corrections to the Altarelli-Parisi evolution equation is necessary.
18244927
Nuclear Shadowing and Antishadowing in a Unitarized BFKL Equation
The nuclear shadowing and antishadowing effects are explained by a unitarized BFKL equation. The Q2and x-variations of the nuclear parton distributions are detailed based on the level of the unintegrated gluon distribution. In particular, the asymptotical behavior of the unintegrated gluon distribution near the saturation limit in nuclear targets is studied. Our results in the nuclear targets are insensitive to the input distributions if the parameters are fixed by the data of a free proton. PACS numbers: 13.60.Hb; 12.38.Bx. keywords: nuclear shadowing and antishadowing; unintegrated gluon distribution in nuclei; QCD evolution equation Supported by National Natural Science Foundation of China 10135060, 10475028 and 10205004. Corresponding author, E-mail: weizhu @mail.ecnu.edu.cn
124473583
Partonic Structure of the Pomeron in the Modified GLR Equation
The gluon recombination effect in the pomeron is studied by using the modified Gribov–Levin–Ryskin evolution equation. We find that the measured parton distributions in the pomeron can be generated dynamically from a purely gluonic input on assuming a small size for the pomeron.
122365434
Bag skin effect for Friedberg-Lee model
Importance of the bag skin effect to determine the parameters of the Friedberg-Lee model is pointed out. The European Muon Collaboration (EMC) effect regarded as a bag skin effect can be used to determine the parameters of this model.
124556870
A New Effect in the QCD Fusion of Nuclear Partons
The parton fusion in nucleus at the leading order of recombination is investigated based on perturbative QCD. We compute various cut diagrams including the nuclear parton fusion, and find that the parton-fusion effects depend on the nuclear QCD structure.
55413960
SEARCHING FOR PULSARS USING IMAGE PATTERN RECOGNITION
In the modern era of big data, many fields of astronomy are generating huge volumes of data, the analysis of which can sometimes be the limiting factor in research. Fortunately, computer scientists have developed powerful data-mining techniques that can be applied to various fields. In this paper, we present a novel artificial intelligence (AI) program that identifies pulsars from recent surveys by using image pattern recognition with deep neural nets—the PICS (Pulsar Image-based Classification System) AI. The AI mimics human experts and distinguishes pulsars from noise and interference by looking for patterns from candidate plots. Different from other pulsar selection programs that search for expected patterns, the PICS AI is taught the salient features of different pulsars from a set of human-labeled candidates through machine learning. The training candidates are collected from the Pulsar Arecibo L-band Feed Array (PALFA) survey. The information from each pulsar candidate is synthesized in four diagnostic plots, which consist of image data with up to thousands of pixels. The AI takes these data from each candidate as its input and uses thousands of such candidates to train its ~9000 neurons. The deep neural networks in this AI system grant it superior ability to recognize various types of pulsars as well as their harmonic signals. The trained AI's performance has been validated with a large set of candidates from a different pulsar survey, the Green Bank North Celestial Cap survey. In this completely independent test, the PICS ranked 264 out of 277 pulsar-related candidates, including all 56 previously known pulsars and 208 of their harmonics, in the top 961 (1%) of 90,008 test candidates, missing only 13 harmonics. The first non-pulsar candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other words, 100% of the pulsars were ranked in the top 1% of all candidates, while 80% were ranked higher than any noise or interference. The performance of this system can be improved over time as more training data are accumulated. This AI system has been integrated into the PALFA survey pipeline and has discovered six new pulsars to date.