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Modeling the Time Evolution of Compact Binary Systems with Machine Learning

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arXiv 2024年

作者： Yan, Jianqi Luo, Junjie Zeng, Yifan Leung, Alex P. Feng, Jie Zhang, Hong-Hao Lin, Weipeng School of Physics Sun Yat-sen University Guangzhou510275 China Faculty of Innovation Engineering Macau University of Science and Technology Avenida Wai Long Taipa999078 China School of Science Sun Yat-sen University Shenzhen Campus Shenzhen518107 China School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Electrical Engineering and Computer Science Oregon State University Oregon CorvallisOR97331 United States Department of Physics The University of Hong Kong 999077 Hong Kong

This work introduces advanced computational techniques for modeling the time evolution of compact binary systems using machine learning. The dynamics of compact binary systems, such as black holes and neutron stars, present significant nonlinear challenges due to the strong gravitational interactions and the requirement for precise numerical simulations. Traditional methods, like the post-Newtonian approximation, often require significant computational resources and face challenges in accuracy and efficiency. Here, we employed machine learning algorithms, including deep learning models like Long Short-Term Memory (LSTM) and Temporal Convolutional Network (TCN), to predict the future evolution of these systems based on extensive simulation data. Our results demonstrate that employing both LSTM and TCN even as black-box predictors for sequence prediction can also significantly improve the prediction accuracy without PINNs as PDE solvers with prior knowledge or inductive bias. By employing LSTM and TCN, we obtained R2 values of 99.74% and 99.19% for the evolutionary orbits of compact binaries dataset, respectively. Our models demonstrate the ability to effectively capture the dynamics of the binaries, achieving high prediction performance with significantly reduced computational overhead by a factor of 40, compared to conventional numerical methods. This study paves the way for more effective and computationally scalable approaches to the understanding of gravitational phenomena and predictive modeling in gravitational-wave astronomy. Copyright © 2024, The Authors. All rights reserved.

关键词： Gravitational effects

Revisiting constraints on proton PDFs from HERA DIS, Drell-Yan, W and Z boson production, and projected EIC measurements

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Physical Review D 2025年第3期111卷 034023-034023页

作者： Majid Azizi Maryam Soleymaninia Hadi Hashamipour Maral Salajegheh Hamzeh Khanpour Ulf-G. Meißner School of Particles and Accelerators Institute for Research in Fundamental Sciences (IPM) P.O. Box 19395-5531 Tehran Iran Istituto Nazionale di Fisica Nucleare Gruppo collegato di Cosenza I-87036 Arcavacata di Rende Cosenza Italy Helmholtz-Institut für Strahlen-und Kernphysik and Bethe Center for Theoretical Physics Universität Bonn D-53115 Bonn Germany AGH University Faculty of Physics and Applied Computer Science Al. Mickiewicza 30 30-055 Krakow Poland Department of Physics University of Science and Technology of Mazandaran P.O. Box 48518-78195 Behshahr Iran Institute for Advanced Simulation (IAS-4) Forschungszentrum Jülich D-52425 Jülich Germany Peng Huanwu Collaborative Center for Research and Education Beihang University Beijing 100191 China

We present new parton distribution functions (PDFs) at next-to-leading order and next-to-next-to-leading order in perturbative QCD, derived from a comprehensive QCD analysis of high-precision datasets from combined HERA deep-inelastic scattering, the Tevatron, and the LHC. To improve constraints on quark flavor separation, we incorporate Drell-Yan pair production data, which provides critical sensitivity to the quark distributions. In addition, we include the latest W and Z boson production data from the CDF, D0, ATLAS, and CMS collaborations, further refining both quark and gluon distributions. Our nominal QCD fit integrates these datasets and examines the resulting impact on the PDFs and their associated uncertainties. Uncertainties in the PDFs are quantified using the Hessian method, ensuring robust error estimates. Furthermore, we explore the sensitivity of the strong coupling constant, αs(MZ2), and proton PDFs in light of the projected measurements from the Electron-Ion Collider, where improvements in precision are expected. The analysis also investigates the effects of inclusive jet and dijet production data, which provide enhanced constraints on the gluon PDF and αs(MZ2).

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Genuine multipartite entanglement detection with imperfect measurements: concept and experiment

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arXiv 2023年

作者： Cao, Huan Morelli, Simon Rozema, Lee A. Zhang, Chao Tavakoli, Armin Walther, Philip Vienna1090 Austria Christian Doppler Laboratory for Photonic Quantum Computer Faculty of Physics University of Vienna Vienna1090 Austria BCAM - Basque Center for Applied Mathematics Mazarredo 14 Basque BilbaoE48009 Spain CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei230026 China CAS Center For Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Hefei230026 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China Physics Department Lund University Box 118 Lund22100 Sweden

Standard procedures for entanglement detection assume that experimenters can exactly implement specific quantum measurements. Here, we depart from such idealizations and investigate, in both theory and experiment, the detection of genuine multipartite entanglement when measurements are subject to small imperfections. For arbitrary qubits number n, we construct multipartite entanglement witnesses where the detrimental influence of the imperfection is independent of n. In a tabletop four-partite photonic experiment we demonstrate first how a small amount of alignment error can undermine the conclusions drawn from standard entanglement witnesses, and then perform the correction analysis. Furthermore, since we consider quantum devices that are trusted but not perfectly controlled, we showcase advantages in terms of noise resilience as compared to device-independent models. © 2023, CC BY.

关键词： Quantum entanglement

Article the effects of various concentrations of naoh on the inter-particle gelation of a fly ash geopolymer aggregate

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Materials 2021年第5期14卷 1-11页

作者： Abdullah, Alida Hussin, Kamarudin Abdullah, Mohd Mustafa Al Bakri Yahya, Zarina Sochacki, Wojciech Razak, Rafiza Abdul Bloch, Katarzyna Fansuri, Hamzah Perlis01000 Malaysia Perlis01000 Malaysia Perlis01000 Malaysia Perlis01000 Malaysia Faculty of Mechanical Engineering and Computer Science Częstochowa University of Technology Częstochowa42-200 Poland Department of Physics Częstochowa University of Technology Częstochowa42-200 Poland in Surabaya Sukolilo60111 Indonesia

Aggregates can be categorized into natural and artificial aggregates. Preserving natural resources is crucial to ensuring the constant supply of natural aggregates. In order to preserve these natural resources, the production of artificial aggregates is beginning to gain the attention of researchers worldwide. One of the methods involves using geopolymer technology. On this basis, this current research focuses on the inter-particle effect on the properties of fly ash geopolymer aggregates with different molarities of sodium hydroxide (NaOH). The effects of synthesis parameters (6, 8, 10, 12, and 14 M) on the mechanical and microstructural properties of the fly ash geopolymer aggregate were studied. The fly ash geopolymer aggregate was palletized manually by using a hand to form a sphere-shaped aggregate where the ratio of NaOH/Na2SiO3 used was constant at 2.5. The results indicated that the NaOH molarity has a significant effect on the impact strength of a fly ash geopolymer aggregate. The highest aggregate impact value (AIV) was obtained for samples with 6 M NaOH molarity (26.95%), indicating the lowest strength among other molarities studied and the lowest density of 2150 kg/m3 . The low concentration of sodium hydroxide in the alkali activator solution resulted in the dissolution of fly ash being limited;thus, the inter-particle volume cannot be fully filled by the precipitated gels. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Sodium hydroxide

Microlensing effects of wormholes associated to blackhole spacetimes

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arXiv 2022年

作者： Gao, Ke Liu, Lei-Hua Zhu, Mian Department of Physics College of Physics Mechanical and Electrical Engineering Jishou University Jishou416000 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Jockey Club Institute for Advanced Study The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow30-348 Poland

In this paper, we investigate the microlensing effects of wormholes associated to black hole spacetimes. Specifically, we work on three typical wormholes (WH): Schwarzschild WH, Kerr WH, and RN WH, as well as their blackhole correspondences. We evaluate the deflection angle upon the second order under weak field approximation using Gauss-Bonnet theorem. Then, we study their magnification with *** find that a Kerr WH could lead to multi peaks in the magnification with certain parameters in the prograde case, while a Kerr BH predicts one peak. Therefore, the multi-peak feature of can be used to distinguish the Kerr WH from other compact objects. We also find that the magnification of RN BH will be one peak compared to RN WH, in which the magnification of RN WH is negative in some situations. For other cases, the behavior of magnification from wormholes and their corresponding blackholes is similar. Our result may shed new light on exploring compact objects through the microlensing effect. © 2022, CC BY.

关键词： Gravitation

Dephasing Noise Simulation for Coherence-Generating Devices

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arXiv 2024年

作者： Salazar, Roberto Shahbeigi, Fereshte Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland International Centre for Theory of Quantum Technologies University of Gdansk Wita Stwosza 63 Gdansk80-308 Poland RCQI Institute of Physics Slovak Academy of Sciences Dúbravská cesta 9 Bratislava84511 Slovakia

Advancing quantum technologies necessitates an in-depth exploration of how operations generate quantum resources and respond to noise. Crucial are gates generating quantum coherence and the challenge of mitigating gate dephasing noise. Precisely, we study the dephasing noise that reduces the coherence-generating power of quantum gates, its simulation, and critical factors. Our primary contribution lies in a theorem characterizing the full set of dephasing noises in gates, adaptable to the simulation by any predefined operation set. In particular, we apply our result to quantify the memory adaptability required for a dephasing noise to arise. Furthermore, we analytically calculate the quantifier for gates acting on qubit systems, thereby fully characterizing this scenario. Next, we show how our results reveal the structure of non-trivial dephasing noise affecting qubit gates and apply them to experimental data, conclusively demonstrating the existence of a gate's dephasing noise, which is irreducible to dephasing of either input or output states. Finally, we show how our study contributes to addressing an open question in the resource theory of coherence generation. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits

Spin-lattice relaxation of NV centers in nanodiamonds adsorbed on conducting and non-conducting surfaces

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arXiv 2025年

作者： Benedičič, Izidor Tanuma, Yuri Gosar, Žiga Anézo, Bastien Mrózek, Mariusz Wojciechowski, Adam Arčon, Denis Jožef Stefan Institute Jamova c. 39 Ljubljana1000 Slovenia Faculty of Mathematics and Physics University of Ljubljana Jadranska c. 19 Ljubljana1000 Slovenia Nantes University 2 Rue de la Houssinière Nantes France Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Prof. S. Lojasiewicza 11 Krakow30-348 Poland

The nitrogen-vacancy (NV) centers in nanodiamonds can be utilized as low-cost, highly versatile quantum sensors for studying surface properties in condensed matter physics through the application of relaxometry protocols. For such applications, a detailed knowledge of the intrinsic relaxation processes of NV centers in nanodiamonds is necessary. Here, we study the spin-lattice relaxation rates of NV ensembles in nanodiamonds with average diameters of 40 nm and 3 µm between room temperature and ∼ 6 K. The NV relaxation curves fit to a stretched-exponential form with a stretching exponent α ≈ 0.7, implying the large distribution of relaxation times of individual centers within nanodiamonds. We determine the Orbach-like scattering on phonons as the leading relaxation mechanism. Finally, we discuss the viability of nanodiamonds as surface sensors when deposited on a metallic substrate and emphasize the need for well-controlled surface preparation techniques. © 2025, CC BY.

关键词： Spin-lattice relaxation

Roll Wear Prediction in Strip Cold Rolling with physics-Informed Autoencoder and Counterfactual Explanations

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Roll Wear Prediction in Strip Cold Rolling with Physics-Info...

International Conference on Data science and Advanced Analytics (DSAA)

作者： Jakub Jakubowski Przemysław Stanisz Szymon Bobek Grzegorz J. Nalepa ArcelorMittal Poland and AGH University of Science and Technology Kraków Poland ArcelorMittal Poland Kraków Poland Jagiellonian Human-Centered Artificial Intelligence Laboratory (JAHCAI) and Institute of Applied Computer Science Jagiellonian University Kraków Poland

ISBN: (纸本)9781665473316

The development of predictive maintenance (PdM) solutions is one of the key challenges in the industry today. Manufacturing processes are usually well described by the law of physics and mathematical equations, but the irregularity and randomness of the asset degradation process make it a demanding task to model it. This makes physics-driven models insufficient for this kind of problem. On the other hand, data-driven models, mainly Artificial Intelligence (AI), are gaining much interest in research and applications due to their flexibility and robustness. A compromise between these two approaches are hybrid models that take into account the physics of the process and use modern AI methods to learn its behavior. The next challenge for AI models is to provide information on their reasoning to build understading and trustworthiness, which can be achieved through post-hoc Explainable AI (XAI) methods. In this paper, we use a physics-Informed Autoencoder (PIAE) in a semi-supervised manner to learn the degradation process of work rolls in the cold- rolling process. We incorporate physics knowledge into the AI model by extending its input space and applying feature masking during the prediction phase. The results of the research show that such an architecture is capable of distinguising between low- and high-wear observations. Furthermore, we include the XAI layer in the model, which gives explanations for the prediction of the model through counterfactuals.

关键词： Degradation Strips Manufacturing processes Predictive models Mathematical models Robustness Artificial intelligence

Analysis, and machine learning anomaly detection of the VELO-LHCb calibration 2

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Analysis, and machine learning anomaly detection of the VELO...

2nd Trans-Siberian School on High Energy physics 2019

作者： Majewski, MacIej Faculty of Physics and Applied Computer Science AGH University of Science and Technology Mickiewicza 30 Krakw Poland

Silicon detectors are an extraordinary piece of equipment that has become the cornerstone of research in modern high energy physics. They are becoming increasingly useful in medical research as well. LHCbs VELO detector is itself, a microstrip silicon vertex detector. It is the heart of LHCb spectrometer and plays a vital role in the reconstruction of particle tracks. The data gathered by VELO is used in LHCb studies of CP violation and heavy flavour physics. This work presents the studies on the calibration parameters of the VELO-LHCb. The inference from the analysis, with the use of probabilistic programming, is then used to create a machine learning based anomaly detection system in the space of VELO calibration. © Published under licence by IOP Publishing Ltd.

关键词： Calibration