arXiv subjects

Qingfeng Li

Publications and source records attributed to Qingfeng Li.

At least 55 records · Page 3Linked to original sources

arXiv2021

Application of machine learning in the determination of impact parameter in the $^{132}$Sn+$^{124}$Sn system

Background: $^{132}$Sn+$^{124}$Sn collisions at the beam energy of 270 MeV$/$nucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing impact parameter is one of the important tasks in the experiment as it relates to many observables. Purpose: In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN) and the light gradient boosting machine (LightGBM), to determine impact parameter by analyzing either the charged particles spectra or several features simulated with events from the ultra-relativistic quantum molecular dynamics (UrQMD) model. Method: To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. Results: The mean absolute error $Δb$ between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, $Δb$ would increase to 0.8 fm. Conclusion: The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct impact parameter in experiment.

nucl-th↗

arXiv2021

Machine learning the nuclear mass

Background: The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z=118) based on various theoretical calculations. Exploring the mass of the remains is a hot topic in nuclear physics. Machine learning has been served as a powerful tool in learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM) which is a highly efficient machine learning algorithm to predict the masses of unknown nuclei and to explore the nuclear landscape in neutron-rich side from learning the measured nuclear masses. Results: By using the experimental data of 80 percent of known nuclei as the training dataset, the root mean square deviation (RMSD) between the predicted and the experimental binding energy of the remaining 20% is about 0.234 MeV, 0.213 MeV, 0.170 MeV, and 0.222 MeV for the LightGBM-refined LDM, DZ, WS4, and FRDM models, respectively. These values are of about 90%, 65%, 40%, and 60% smaller than the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 is also significantly improved on the same foot. One-neutron and two-neutron separation energies predicted by these refined models are in consistence with several theoretical predictions based on various physical models. Conclusions: LightGBM can be used to refine theoretical nuclear mass models so as to predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley Additive exPlanations (SHAP, a popular explainable artificial intelligence tool), this may provide new insights on developing theoretical nuclear mass models.

nucl-th↗

arXiv2021

Proton correlations and apparent intermittency in the UrQMD model with hadronic potentials

It is shown that the inclusion of hadronic interactions, and in particular nuclear potentials, in simulations of heavy ion collisions at the SPS energy range can lead to obvious correlations of protons. These correlations contribute significantly to an intermittency analysis as performed at the NA61 experiment. The beam energy and system size dependence is studied by comparing the resulting intermittency index for heavy ion collisions of different nuclei at beam energies of $40A$, $80A$ and $150A$ GeV. The resulting intermittency index from our simulations is similar to the reported values of the NA61 collaboration, if nuclear interactions are included. The observed apparent intermittency signal is the result of the correlated proton pairs with small relative transverse momentum $Δp_{t}$, which would be enhanced by hadronic potentials, and this correlation between the protons is slightly influenced by the coalescence parameters and the relative invariant four-momentum $q_{inv}$ cut.

nucl-th↗

arXiv2021

A flexible, fast and benchmarked vectorial model for focused laser beams

In-bulk processing of materials by laser radiation has largely evolved over the last decades and still opensup new scientific and industrial potentials. The development of any in-bulk processing application relieson the knowledge of laser propagation and especially the volumetric field distribution near the focus.Many commercial programs can simulate this, but, in order to adapt them, or to develop new methods,one usually needs to create a specific software. Besides, most of the time people also need to measurethe actual field distribution near the focus to evaluate their assumptions in the simulation. To easily get access to this knowledge, we present our high-precision field distribution measuring method and release our in-house software InFocus, under the Creative Commons 4.0 License. Our measurementsprovide 300-nm longitudinal resolution and diffraction limited lateral resolution. The in-house softwareallows fast vectorial analysis of the focused volumetric field distribution in the bulk. The simulationsof light propagation under different conditions (focusing optics, wavelength, spatial shape, propagationmedium) are in excellent agreement with propagation imaging experiments. The aberrations provoked by the refractive index mismatch as well as those induced by the focusing optics are both taken into account.The results indicate that our proposed model is suitable for the precise evaluation of energy deposition

physics.optics↗

arXiv2020

MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing

Manga is a world popular comic form originated in Japan, which typically employs black-and-white stroke lines and geometric exaggeration to describe humans' appearances, poses, and actions. In this paper, we propose MangaGAN, the first method based on Generative Adversarial Network (GAN) for unpaired photo-to-manga translation. Inspired by how experienced manga artists draw manga, MangaGAN generates the geometric features of manga face by a designed GAN model and delicately translates each facial region into the manga domain by a tailored multi-GANs architecture. For training MangaGAN, we construct a new dataset collected from a popular manga work, containing manga facial features, landmarks, bodies, and so on. Moreover, to produce high-quality manga faces, we further propose a structural smoothing loss to smooth stroke-lines and avoid noisy pixels, and a similarity preserving module to improve the similarity between domains of photo and manga. Extensive experiments show that MangaGAN can produce high-quality manga faces which preserve both the facial similarity and a popular manga style, and outperforms other related state-of-the-art methods.

cs.CV↗

arXiv2020

An End-to-end Method for Producing Scanning-robust Stylized QR Codes

Quick Response (QR) code is one of the most worldwide used two-dimensional codes.~Traditional QR codes appear as random collections of black-and-white modules that lack visual semantics and aesthetic elements, which inspires the recent works to beautify the appearances of QR codes. However, these works adopt fixed generation algorithms and therefore can only generate QR codes with a pre-defined style. In this paper, combining the Neural Style Transfer technique, we propose a novel end-to-end method, named ArtCoder, to generate the stylized QR codes that are personalized, diverse, attractive, and scanning-robust.~To guarantee that the generated stylized QR codes are still scanning-robust, we propose a Sampling-Simulation layer, a module-based code loss, and a competition mechanism. The experimental results show that our stylized QR codes have high-quality in both the visual effect and the scanning-robustness, and they are able to support the real-world application.

cs.CV↗

arXiv2020

Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the Convolutional Neural Networks (CNN) and Light Gradient Boosting Machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au+Au collisions with the impact parameter of 0$\leq$$b$$\leq$10 fm at intermediate energies ($E_{\rm lab}$=$0.2$-$1.0$ GeV$/$nucleon) are simulated with the ultrarelativistic quantum molecular dynamics (UrQMD) model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments.

nucl-th↗

arXiv2020

Insights on pion production mechanism and symmetry energy at high density

The $NΔ\to NN$ cross sections, which take into account the $Δ$-mass dependence of M-matrix and momentum $p_{NΔ}$, are applied on the calculation of pion production within the framework of the UrQMD model. Our study shows that UrQMD calculations with the $Δ$-mass dependent $NΔ\to NN$ cross sections enhance the pion multiplicities and decrease the $π^-/π^+$ ratios. By analyzing the time evolution of the pion production rate and the density in the overlapped region for Au+Au at the beam energy of 0.4A GeV, we find that the pion multiplicity probes the symmetry energy in the region of 1-2 times normal density. The process of pion production in the reaction is tracked including the loops of $NN\leftrightarrow NΔ$ and $Δ\leftrightarrow Nπ$, our calculations show that the sensitivity of $π^-/π^+$ to symmetry energy is weakened after 4-5 N-$Δ$-$π$ loops in the pion production path, while the $π^{-}/π^{+}$ ratio in reactions at near threshold energies remains its sensitivity to the symmetry energy. By comparing the calculations to the FOPI data, we obtain a model dependent conclusion on the symmetry energy and the symmetry energy at two times normal density is $S(2ρ_0)$=38-73 MeV within $1σ$ uncertainties. Under the constraints of tidal deformability and maximum mass of neutron star, the symmetry energy at two times normal density is reduced to $48-58$ MeV and slope of symmetry energy $L=54-81$ MeV, and it is consistent with the constraints from ASY-EOS flow data.

nucl-th↗

arXiv2020

Progress of Quantum Molecular Dynamics model and its applications in Heavy Ion Collisions

In this review article, we first briefly introduce the transport theory and quantum molecular dynamics model applied in the study of the heavy ion collisions from low to intermediate energies. The developments of improved quantum molecular dynamics model (ImQMD) and ultra-relativistic quantum molecular dynamics model (UrQMD), are reviewed. The reaction mechanism and phenomena related to the fusion, multinucleon transfer, fragmentation, collective flow and particle production are reviewed and discussed within the framework of the two models. The constraints on the isospin asymmetric nuclear equation of state and in-medium nucleon-nucleon cross sections by comparing the heavy ion collision data with transport models calculations in last decades are also discussed, and the uncertainties of these constraints are analyzed as well. Finally, we discuss the future direction of the development of the transport models for improving the understanding of the reaction mechanism, the descriptions of various observables, the constraint on the nuclear equation of state, as well as for the constraint on in-medium nucleon-nucleon cross sections.

nucl-th↗

arXiv2020

Application of microscopic transport model in the study of nuclear equation of state from heavy ion collisions at intermediate energies

The equation of state (EOS) of nuclear matter, i.e., the thermodynamic relationship between the binding energy per nucleon, temperature, density, as well as the isospin asymmetry, has been a hot topic in nuclear physics and astrophysics for a long time. The knowledge of the nuclear EOS is essential for studying the properties of nuclei, the structure of neutron stars, the dynamics of heavy ion collision (HIC), as well as neutron star mergers. HIC offers a unique way to create nuclear matter with high density and isospin asymmetry in terrestrial laboratory, but the formed dense nuclear matter exists only for a very short period, one cannot measure the nuclear EOS directly in experiments. Practically, transport models which often incorporate phenomenological potentials as an input are utilized to deduce the EOS from the comparison with the observables measured in laboratory. The ultrarelativistic quantum molecular dynamics (UrQMD) model has been widely employed for investigating HIC from the Fermi energy (40 MeV per nucleon) up to the CERN Large Hadron Collider energies (TeV). With further improvement in the nuclear mean-field potential term, the collision term, and the cluster recognition term of the UrQMD model, the newly measured collective flow and nuclear stopping data of light charged particles by the FOPI Collaboration can be reproduced. In this article we highlight our recent results on the studies of the nuclear EOS and the nuclear symmetry energy with the UrQMD model. New opportunities and challenges in the extraction of the nuclear EOS from transport models and HIC experiments are discussed.

nucl-th↗

arXiv2020

Beam energy dependence of cumulants of the net-baryon, net-charge and deuteron multiplicity distributions in Au+Au collisions at $\sqrt{s_{NN}}=3.0-5.0$ GeV

Within the ultra-relativistic quantum molecular dynamics (UrQMD) model, in which the Lorentz-covariant treatment of nuclear mean-field potential is considered, the fluctuations of net-baryon, net-charge and deuterons multiplicity distributions in Au+Au head-on collisions at $\sqrt{s_{NN}}=3.0-5.0$ GeV are calculated. The results show that the nuclear mean-field potential can significantly enhance the magnitude of baryon number fluctuations in narrow rapidity windows, and this enhancement rapidly weakens with increasing beam energy. However, for proton and net-charge number fluctuations, the mean-field effects are less noticeable than that for baryon number. In addition, for net-charge number fluctuations, the negative binomial distribution agrees well with the calculated results at mid-pseudorapidity window. Finally, the event-by-event fluctuations of deuteron number in the coalescence production picture are calculated as well, it is found that its cumulant ratios decrease linearly with increasing the average multiplicity of deuterons per event, i.e., increase with increasing beam energy.

nucl-th↗

arXiv2020

Elliptic flow splitting between protons and antiprotons from hadronic potentials

The difference in elliptic flow $v_{2}$ between protons and antiprotons, produced in $^{197}\text{Au}+^{197}\text{Au}$ collisions at center-of-mass energies $\sqrt{s_{NN}}=5-12~\text{GeV}$, is studied within a modified version of the ultrarelativistic quantum molecular dynamics (UrQMD) model. Two different model scenarios are compared: the cascade mode and the mean field mode which includes potential interactions for both formed and pre-formed hadrons. The model results for the elliptic flow of protons and the relative $v_{2}$ difference between protons and antiprotons obtained from the mean field mode agree with the available experimental data, while the $v_{2}$ difference is near zero for the cascade mode. Our results show that the elliptic flow splitting, observed for particles and antiparticles, can be explained by the inclusion of proper hadronic interactions. In addition, the difference in $v_{2}$ between protons and antiprotons depends on the centrality and the rapidity window. With smaller centrality and/or rapidity acceptance, the observed elliptic flow splitting is more sensitive to the beam energy, indicating a strong net baryon density dependence of the effect. We propose to confirm this splitting at the upcoming experiments from Beam Energy Scan (BES) Phase-\Rmnum{2} at Relativistic Heavy Ion Collider (RHIC), the Compressed Baryonic Matter (CBM) at Facility for Antiproton and Ion Research (FAIR), High Intensity heavy ion Accelerator Facility (HIAF) and Nuclotron-based Ion Collider fAcility (NICA).

nucl-th↗

arXiv2019

DS-GCNs: Connectome Classification Using Dynamic Spectral Graph Convolution Networks with Assistant Task Training

Functional Connectivity (FC) matrices measure the regional interactions in the brain and have been widely used in neurological brain disease classification. However, a FC matrix is neither a natural image which contains shape and texture information, nor a vector of independent features, which renders the extracting of efficient features from matrices as a challenging problem. A brain network, also named as connectome, could forma a graph structure naturally, the nodes of which are brain regions and the edges are interregional connectivity. Thus, in this study, we proposed novel graph convolutional networks (GCNs) to extract efficient disease-related features from FC matrices. Considering the time-dependent nature of brain activity, we computed dynamic FC matrices with sliding-windows and implemented a graph convolution based LSTM (long short term memory) layer to process dynamic graphs. Moreover, the demographics of patients were also used to guide the classification. However, unlike in conventional methods where personal information, i.e., gender and age were added as extra inputs, we argue that this kind of approach may not actually improve the classification performance, for such personal information given in dataset was usually balanced distributed. In this paper, we proposed to utilize the demographic information as extra outputs and to share parameters among three networks predicting subject status, gender and age, which serve as assistant tasks. We tested the performance of the proposed architecture in ADNI II dataset to classify Alzheimer's disease patients from normal controls. The classification accuracy, sensitivity and specificity reach 0.90, 0.92 and 0.89 on ADNI II dataset.

physics.med-ph↗

arXiv2019

Percentile-Based Residuals for Model Assessment

Residuals are a key component of diagnosing model fit. The usual practice is to compute standardized residuals using expected values and standard deviations of the observed data, then use these values to detect outliers and assess model fit. Approximate normality of these residuals is key for this process to have good properties, but in many modeling contexts, especially for complex, multi-level models, normality may not hold. In these cases outlier detection and model diagnostics aren't properly calibrated. Alternatively, as we demonstrate, residuals computed from the percentile location of a datum's value in its full predictive distribution lead to well calibrated evaluations of model fit. We generalize an approach described by Dunn and Smyth (1996) and evaluate properties mathematically, via case-studies and by simulation. In addition, we show that the standard residuals can be calibrated to mimic the percentile approach, but that this extra step is avoided by directly using percentile-based residuals. For both the percentile-based residuals and the calibrated standard residuals, the use of full predictive distributions with the appropriate location, spread and shape is necessary for valid assessments.

stat.ME↗

arXiv2019

The effect of internal magnetic field on collective flow in heavy ion collisions at intermediate energies

The properties of nuclear matter under extreme conditions of high temperature, density and isospin-asymmetry have attracted wide attentions in recent years. At present, heavy ion reactions in combination with corresponding model simulations are one of the most important ways to investigate this subject. It is known that a strong magnetic field can be created in heavy ion collisions. However, its effect on the motion of charged particles is usually neglected in previous transport model simulations. In this work, within the Ultra-relativistic Quantum Molecular Dynamics (UrQMD) model, the temporal evolution and spatial distribution of the internal magnetic field are calculated. The magnetic field strength is found to reach about $eB\approx470$ MeV$^{2}$ ($B\approx8\times10^{16}$ G) for Au+Au collisions at $E_{\text{lab}}$=1 GeV/nucleon with impact parameter of 7 fm. The magnetic field in Cu+Au collisions exhibits somewhat different spatial distribution from that in Au+Au collisions. The magnetic field is found to affect the directed flow of pions at forward and backward rapidities to some extent, dependent of the impact parameter and beam energy while the effect on the elliptic flow is small. This suggests that, because $π$ mesons produced in heavy ion collisions at intermediate energies are considered as a sensitive probe for the nuclear symmetry energy, it is necessary to consider the effect of the internal magnetic field.

nucl-th↗

arXiv2018

The cumulants of the baryon number from central Au+Au collision at $E_{lab}$= 1.23 GeV$/$nucleon reveal the nuclear mean-field potentials

Fluctuations of the baryon number in relativistic heavy-ion collisions are a promising observable to explore the structure of the QCD phase diagram. The cumulant ratios in heavy ion collisions at intermediate energies ($\sqrt{s_{\textrm{NN}}} < 7$ GeV) have not been studied to date. We investigate the effects of mean field potential and clustering on the cumulant ratios of baryon and proton number distributions in Au+Au collisions at beam energy of 1.23 GeV$/$nucleon as measured by the HADES Collaboration at GSI. Ultrarelativistic Quantum Molecular Dynamics (UrQMD) and the JAM model are used to calculate the cumulants with different mean field potentials. It is found that the cumulant ratios are strongly time dependent. At the early stage, the effects of the potentials on the fluctuations of the particle multiplicity in momentum space are relatively weak. The mean fields enhance the fluctuations during the expansion stage, especially for small rapidity acceptance windows. The enhancement of cumulant ratios for free protons is strongly suppressed as compared to that for all baryons. The mean field potentials and the clustering play an important role for the measured cumulant ratios at intermediate energy.

nucl-th↗

arXiv2018

Nuclear interactions and net-proton number fluctuations in heavy ion collisions at the SIS18 accelerator

The effect of nuclear interactions on measurable net-proton number fluctuations in heavy ion collisions at the SIS18/GSI accelerator is investigated. The state of the art UrQMD model including interaction potentials is employed. It is found that the nuclear forces enhance the baryon number cumulants, as predicted from grand canonical thermodynamical models. The effect however is smeared out for proton number fluctuations due to iso-spin randomization and global baryon number conservation, which decreases the cumulant ratios. For a rapidity acceptance window larger than $Δy> 0.4$ the effects of global baryon number conservation dominate and all cumulant ratios are significantly smaller than 1.

nucl-th↗

arXiv2018

The effect of Lorentz-like force on collective flows of K$^{+}$ in Au+Au collisions at 1.5 GeV/nucleon}{The effect of Lorentz-like force on collective flows of $K^{+}$ in Au+Au collisions at 1.5 GeV/nucleon

Producing kaon mesons in heavy-ion collisions at beam energies below their threshold energy is an important way to investigate the properties of dense nuclear matter. In this study, based on the newly updated version of the ultrarelativistic quantum molecular dynamics model, we introduce the kaon-nucleon (KN) potential, including both the scalar and vector (also dubbed Lorentz-like) aspects. We revisit the influence of the KN potential on the collective flow of K$^{+}$ mesons produced in Au+Au collisions at $E_{lab}$ = 1.5 GeV/nucleon and find that the contribution of the newly included Lorentz-like force is very important, particulary for describing the directed flow of K$^{+}$. Finally, the corresponding KaoS data of both directed and elliptic flows can be simultaneously reproduced well.

nucl-th↗