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Taiji-TianQin-LISA network:Precisely measuring the Hubble constant using both bright and dark sirens

Science China(Physics,Mechanics & Astronomy) 2024年第2期67卷 59-77页

作者： Shang-Jie Jin Ye-Zhu Zhang Ji-Yu Song Jing-Fei Zhang Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

In the coming decades,the space-based gravitational-wave(GW)detectors such as Taiji,TianQin,and LISA are expected to form a network capable of detecting millihertz GWs emitted by the mergers of massive black hole binaries(MBHBs).In this work,we investigate the potential of GW standard sirens from the Taiji-TianQin-LISA network in constraining cosmological *** the optimistic scenario in which electromagnetic(EM)counterparts can be detected,we predict the number of detectable bright sirens based on three different MBHB population models,i.e.,popⅢ,Q3d,and *** results show that the TaijiTianQin-LISA network alone could achieve a constraint precision of 0.9%for the Hubble constant,meeting the standard of precision ***,the Taiji-TianQin-LISA network could effectively break the cosmological parameter degeneracies generated by the CMB data,particularly in the dynamical dark energy *** combined with the CMB data,the joint CMB+Taiji-TianQin-LISA data offerσ(w)=0.036 in the wCDM model,which is close to the latest constraint result obtained from the CMB+SN *** also consider a conservative scenario in which EM counterparts are not *** to the precise sky localizations of MBHBs by the Taiji-TianQin-LISA network,the constraint precision of the Hubble constant is expected to reach 1.2%.In conclusion,the GW standard sirens from the Taiji-TianQin-LISA network will play a critical role in helping solve the Hubble tension and shedding light on the nature of dark energy.

关键词： space-based gravitational wave detection standard sirens the Taiji-TianQin-LISA network the Hubble constant dark energy

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Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning

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Chinese Physics C 2024年第12期48卷 230-242页

作者： Yu-Xin Wang Shang-Jie Jin Tian-Yang Sun Jing-Fei Zhang Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Recent developments in deep learning techniques have provided alternative and complementary approaches to the traditional matched-filtering methods for identifying gravitational wave(GW)*** rapid and accurate identification of GW signals is crucial to the advancement of GW physics and multi-messenger astronomy,particularly considering the upcoming fourth and fifth observing runs of *** this study,we used the 2D U-Net algorithm to identify time-frequency domain GW signals from stellar-mass binary black hole(BBH)*** simulated BBH mergers with component masses ranging from 7 to 50 M_(⊙)and accounted for the LIGO detector *** found that the GW events in the first and second observation runs could all be clearly and rapidly *** the third observing run,approximately 80% of the GW events could be *** contrast to traditional convolutional neural networks,the U-Net algorithm can output time-frequency domain signal images corresponding to probabilities,providing a more intuitive *** conclusion,the U-Net algorithm can rapidly identify the time-frequency domain GW signals from BBH mergers.

关键词： gravitational waves deep learning binary black holes time-frequency domain signals U-Net algorithm

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Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

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Chinese Physics C 2024年第4期48卷 240-251页

作者：孙天阳熊春雨金上捷王钰鑫张敬飞张鑫 Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW *** inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such *** this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency ***,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling ***,our approach exhibits a greater efficiency,boasting processing times on the order of *** conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

关键词： gravitational wave glitch non-Gaussian and transient noise normalizing flow machine learning likelihood-free parameter inference

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MOEA/D With Spatial-Temporal Topological Tensor Prediction for Evolutionary Dynamic Multiobjective optimization

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Xianpeng Zhao, Yumeng Tang, Lixin Yao, Xin Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang China Lingnan University Hong Kong China

When solving dynamic multiobjective optimization problems, most evolutionary algorithms attempt to predict the initial population in a new environment by mining the relationships between solutions during historical environment changes. However, the complex relationships between solutions and the limited amount of available data often make it difficult to extract useful information efficiently, which may deteriorate the prediction accuracy. To address this problem, this paper proposes a spatial-temporal topological tensor-based prediction method to generate the initial population in a new environment under the decomposition framework of MOEA/D. The method relies on the idea that the population distribution in each environment has topological similarity along the time dimension in the objective space, which makes it efficient to represent the population distribution in terms of a tensor and predict new solutions along each decomposition axis in a new environment by an improved tensor-based multi-short time series prediction method. Experimental results on various benchmark problems and a real-world problem show that the proposed method is competitive or even superior to state-of-the-art dynamic multiobjective evolutionary algorithms based on prediction strategies. IEEE

关键词： Tensors

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Electrochemically activated nickel-cobalt double hydroxide for aqueous ammonium-zinc hybrid battery

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Nano Research 2023年第2期16卷 2495-2501页

作者： Qing Pan Peng Hei Yu Song Jianming Meng Chang Liu Xiao-Xia Liu Department of Chemistry Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China

Aqueous rechargeable ammonium-ion(NH^(4+))batteries(AAIBs)with ammonium ions as charge carriers possess many advantages,yet the relatively low discharge capacities(e.g.,<200 mAh·g^(−1))of the reported NH_(4)^(+)host materials hinder the development of ***,we study the NH_(4)^(+)storage properties of an electrochemically activated NiCo double hydroxide(A-NiCo-DH)in neutral ammonium acetate electrolyte for the first *** activation process extracts the interlayer anions(NO3from the host material,providing additional cation accommodation sites for charge *** introduced H vacancies in A-−)NiCo-DH could activate the O sites,leading to the enhanced cation adsorption capability for the ***,A-NiCo-DH exhibits a high discharge capacity of 280.6 mAh·g^(−1)at 0.72 A·g^(−1)with good rate *** studies suggest A-NiCo-DH experiences a NH4+/H+coinsertion mechanism.A NH4+-Zn hybrid cell is assembled using A-NiCo-DH as the cathode and Zn foil as the anode,*** device delivers an energy density of 306 Wh·kg^(−1)at the power density of 745.8 W·kg^(−1)(based on the active mass of A-NiCo-DH).This work provides a new NH_(4)^(+)storage material and would push forward the development of aqueous NH4+-based batteries.

关键词： Ni-Co double hydroxide electrochemical activation charge storage mechanism high capacity ammonium-ion battery

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Application of XGBoost and kernel principal component analysis to forecast oxygen content in ESR

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Journal of Iron and Steel Research International 2024年第12期31卷 2940-2952页

作者： Yu-xiao Liu Yan-wu Dong Zhou-hua Jiang Qi Wang Yu-shuo Li School of Metallurgy Northeastern UniversityShenyang110819LiaoningChina National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang110819LiaoningChina Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang110000LiaoningChina Key Laboratory of Ecological Metallurgy of Multimetallic Mineral Northeastern UniversityShenyang110819LiaoningChina

A model combining kernel principal component analysis(KPCA)and Xtreme Gradient Boosting(XGBoost)was introduced for forecasting the final oxygen content of electroslag *** was employed to reduce the dimensionality of the factors influencing the endpoint oxygen content and to eliminate any existing correlations among these *** resulting principal components were then utilized as input variables for the XGBoost prediction *** KPCA-XGBoost model was trained and proven using data obtained from *** model structure was adapted,and hyperparameters were optimized using grid search *** model performance of the KPCA-XGBoost model is compared with five machine learning models,including the support vector regression *** findings demonstrated that the KPCA-XGBoost model exhibited the highest level of prediction accuracy,indicating that the incorporation of KPCA significantly enhanced the regression prediction performance of the *** accuracy of the KPCA-XGBoost model was 82.4%,97.1%,and 100%at errors of±1.5×10^(-6),±2.0×10^(-6),and±3×10^(-6)for oxygen content,respectively.

关键词： Electroslag remelting Oxygen content Machine learning Kernel principal component analysis XGBoost

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Voter model on adaptive networks

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Chinese Physics B 2022年第5期31卷 780-790页

作者： Jinming Du Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China Institute of Industrial and Systems Engineering College of Information Science and EngineeringNortheastern UniversityShenyang 110819China Liaoning Engineering Laboratory of Operations Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

Voter model is an important basic model in statistical *** recent years,it has been more and more used to describe the process of opinion formation in *** real complex systems,the interactive network of individuals is dynamically adjusted,and the evolving network topology and individual behaviors affect each ***,we propose a linking dynamics to describe the coevolution of network topology and individual behaviors in this paper,and study the voter model on the adaptive *** theoretically analyze the properties of the voter model,including consensus probability and *** evolution of opinions on dynamic networks is further analyzed from the perspective of evolutionary ***,a case study of real data is shown to verify the effectiveness of the theory.

关键词： statistical physics opinion dynamics evolutionary game theory complex systems

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Bi-objective approach for lot-sizing problem in cold rolling production planning

Bi-objective approach for lot-sizing problem in cold rolling...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Jia, Weiyan Tang, Lixin Yang, Yang Dong, Yun National Frontiers Science Center for Industrial Intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

Planning in the cold rolling is an increasingly important vital aspect in steel industry. In this study, we investigate a cold rolling planning problem derived from a steel company, which is characterized by multi-item considerations with sequence-dependent setups in a single period. To tackle this problem, we introduce a bi-objective modified lot-sizing model, aiming at minimizing both the capacity waste and setup cost. A dedicated multi-objective evolutionary algorithm is proposed to solve this problem. The experiment results demonstrate the effectiveness of the proposed algorithm in achieving near-optimal solutions within a reasonable time, which can facilitate reductions in both production costs and time. © 2024 Copyright held by the owner/author(s).

关键词： evolutionary algorithm

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Forecast for cosmological parameter estimation with gravitational-wave standard sirens from the LISA-Taiji network

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Science China(Physics,Mechanics & Astronomy) 2022年第1期65卷 1-13页

作者： Ling-Feng Wang Shang-Jie Jin Jing-Fei Zhang Xin Zhang Department of Physics College of SciencesNortheastern UniversityShenyang 110819China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern UniversityShenyang 110819China

LISA and Taiji are expected to form a space-based gravitational-wave(GW)detection network in the *** this work,we make a forecast for the cosmological parameter estimation with the standard siren observation from the LISA-Taiji *** simulate the standard siren data based on a scenario with configuration angle of 40°between LISA and *** models for the population of massive black hole binary(MBHB),i.e.,popⅢ,Q3d,and Q3nod,are considered to predict the events of MBHB *** find that,based on the LISA-Taiji network,the number of electromagnetic(EM)counterparts detected is almost doubled compared with the case of single Taiji ***,the LISA-Taiji network’s standard siren observation could provide much tighter constraints on cosmological *** example,solely using the standard sirens from the LISA-Taiji network,the constraint precision of H;could reach 1.3%.Moreover,combined with the CMB data,the GW-EM observation based on the LISA-Taiji network could also tightly constrain the equation of state of dark energy,e.g.,the constraint precision of w reaches about 4%,which is comparable with the result of CMB+BAO+*** is concluded that the GW standard sirens from the LISA-Taiji network will become a useful cosmological probe in understanding the nature of dark energy in the future.

关键词： LISA-Taiji network gravitational-wave standard siren cosmological parameter estimation massive black hole binary electromagnetic counterpart

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Prospects for measuring dark energy with 21 cm intensity mapping experiments:A joint survey strategy

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Science China(Physics,Mechanics & Astronomy) 2023年第7期66卷 38-47页

作者： Peng-Ju Wu Yichao Li Jing-Fei Zhang Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning Province)&Department of Physics College of SciencesNortheastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China

The 21 cm intensity mapping(IM)technique provides us with an efficient way to observe the cosmic large-scale structure(LSS).From the LSS data,one can use the baryon acoustic oscillation and redshift space distortion to trace the expansion and growth history of the universe,and thus measure the dark energy *** this paper,we make a forecast for cosmological parameter estimation with the synergy of three 21 cm IM ***,we adopt a novel joint survey strategy,FAST(0

关键词： neutral hydrogen sky survey 21 cm intensity mapping dark energy large-scale structure joint survey strategy

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