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arXiv 2020年

作者： Banjai, Lehel Melenk, Jens M. Schwab, Christoph Maxwell Institute for Mathematical Sciences School of Mathematical & Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Institut für Analysis und Scientific Computing Technische Universität Wien ViennaA-1040 Austria Seminar for Applied Mathematics ETH Zürich ETH Zentrum HG G57.1 ZürichCH8092 Switzerland

For the spectral fractional diffusion operator of order 2s ∈ (0,2) in bounded, curvilinear polygonal domains Ω ⊂ R2 we prove exponential convergence of two classes of hp discretizations under the assumption of analytic data (coefficients and source terms, without any boundary compatibility), in the natural fractional Sobolev norm Hs(Ω). The first hp discretization is based on writing the solution as a co-normal derivative of a 2 + 1-dimensional local, linear elliptic boundary value problem, to which an hp-FE discretization is applied. A diagonalization in the extended variable reduces the numerical approximation of the inverse of the spectral fractional diffusion operator to the numerical approximation of a system of local, decoupled, second order reaction-diffusion equations in Ω. Leveraging results on robust exponential convergence of hp-FEM for second order, linear reaction diffusion boundary value problems in Ω, exponential convergence rates for solutions u ∈ Hs(Ω) of Lsu = f follow. Key ingredient in this hp-FEM are boundary fitted meshes with geometric mesh refinement towards ∂Ω. The second discretization is based on exponentially convergent numerical sinc quadrature approximations of the Balakrishnan integral representation of L−s combined with hp-FE discretizations of a decoupled system of local, linear, singularly perturbed reaction-diffusion equations in Ω. The present analysis for either approach extends to (polygonal subsets Mf of) analytic, compact 2-manifolds M, parametrized by a global, analytic chart χ with polygonal Euclidean parameter domain Ω ⊂ R2. Numerical experiments for model problems in nonconvex polygonal domains and with incompatible data confirm the theoretical results. Exponentially small bounds on Kolmogoroff n-widths of solutions sets for spectral fractional diffusion in polygons are *** Codes 26A33, 65N12, 65N30 Copyright © 2020, The Authors. All rights reserved.

关键词： Linear equations

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Toward the end-to-end optimization of particle physics instruments with differentiable programming

引用

Reviews in Physics 2023年 10卷

作者： Dorigo, Tommaso Giammanco, Andrea Vischia, Pietro Aehle, Max Bawaj, Mateusz Boldyrev, Alexey de Castro Manzano, Pablo Derkach, Denis Donini, Julien Edelen, Auralee Fanzago, Federica Gauger, Nicolas R. Glaser, Christian Baydin, Atılım G. Heinrich, Lukas Keidel, Ralf Kieseler, Jan Krause, Claudius Lagrange, Maxime Lamparth, Max Layer, Lukas Maier, Gernot Nardi, Federico Pettersen, Helge E.S. Ramos, Alberto Ratnikov, Fedor Röhrich, Dieter de Austri, Roberto Ruiz del Árbol, Pablo Martínez Ruiz Savchenko, Oleg Simpson, Nathan Strong, Giles C. Taliercio, Angela Tosi, Mia Ustyuzhanin, Andrey Zaraket, Haitham MODE Collaboration Istituto Nazionale di Fisica Nucleare Sezione di Padova Italy Université catholique de Louvain Belgium Chair for Scientific Computing Technische Universität Kaiserslautern Germany Università di Perugia and INFN Sezione di Perugia Italy HSE University Russia Université Clermont Auvergne Laboratoire de Physique de Clermont CNRS/IN2P3 France SLAC National Accelerator Laboratory United States Department of Physics and Astronomy Uppsala University Sweden Department of Computer Science University of Oxford United Kingdom Physik-Department Technische Universität München Germany Center for Technology and Transfer University of Applied Sciences Worms Germany CERN Switzerland NHETC Department of Physics and Astronomy Rutgers University United States Università di Napoli "Federico II" Italy Germany Università degli Studi di Padova Italy Department of Oncology and Medical Physics Haukeland University Hospital Norway Instituto de Física Corpuscular UV-CSIC Spain Department of Physics and Technology University of Bergen Norway Instituto de Física de Cantabria UC-CSIC Spain Lund University Sweden Multi-Disciplinary Physics Laboratory Optics and Fiber Optics Group Faculty of Sciences Lebanese University Lebanon the World Constructor University Bremen gGmbH Campus Ring 1 Bremen 28759 Germany

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, "experience-driven" layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. © 2023 The Author(s)

关键词： Astrophysics

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HP-FEM FOR REACTION-DIFFUSION EQUATIONS. II: ROBUST EXPONENTIAL CONVERGENCE FOR MULTIPLE LENGTH SCALES IN CORNER DOMAINS.

arXiv

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arXiv 2020年

In bounded, polygonal domains Ω ⊂ R2 with Lipschitz boundary ∂Ω consisting of a finite number of Jordan curves admitting analytic parametrizations, we analyze hp-FEM discretizations of linear, second order, singularly perturbed reaction diffusion equations on so-called geometric boundary layer meshes. We prove, under suitable analyticity assumptions on the data, that these hp-FEM afford exponential convergence in the natural "energy" norm of the problem, as long as the geometric boundary layer mesh can resolve the smallest length scale present in the problem. Numerical experiments confirm the robust exponential convergence of the proposed *** Codes 65N30, 65N12 Copyright © 2020, The Authors. All rights reserved.

关键词： Mesh generation

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Uplink-downlink duality between multiple-access and broadcast channels with compressing relays

arXiv

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arXiv 2020年

作者： Liu, Liang Liu, Ya-Feng Patil, Pratik Yu, Wei the Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong the State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Edward S. Rogers Sr. Department of Electrical and Computer Engineering the University of Toronto the Department of Statistics and Data Science and the Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States The Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto 10 King’s College Road TorontoONM5S3G4 Canada

—Uplink-downlink duality refers to the fact that under a sum-power constraint, the capacity regions of a Gaussian multiple-access channel and a Gaussian broadcast channel with Hermitian transposed channel matrices are identical. This paper generalizes this result to a cooperative cellular network, in which remote access-points are deployed as relays in serving the users under the coordination of a central processor (CP). In this model, the users and the relays are connected over noisy wireless links, while the relays and the CP are connected over noiseless but rate-limited fronthaul links. Based on a Lagrangian technique, this paper establishes a duality relationship between such a multiple-access relay channel and broadcast relay channel, under the assumption that the relays use compression-based strategies. Specifically, we show that under the same total transmit power constraint and individual fronthaul rate constraints, the achievable rate regions of the Gaussian multiple-access and broadcast relay channels are identical, when either independent compression or Wyner-Ziv and multivariate compression strategies are used. The key observations are that if the beamforming vectors at the relays are fixed, the sum-power minimization problems under the achievable rate and fronthaul constraints in both the uplink and the downlink can be transformed into either a linear programming or a semidefinite programming problem depending on the compression technique, and that the uplink and downlink problems are Lagrangian duals of each other. Moreover, the dual variables corresponding to the downlink rate constraints become the uplink powers;the dual variables corresponding to the downlink fronthaul constraints become the uplink quantization noises. This duality relationship enables an efficient algorithm for optimizing the downlink transmission and relaying strategies based on the uplink. Copyright © 2020, The Authors. All rights reserved.

关键词： Linear programming

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Optimal provable robustness of quantum classification via quantum hypothesis testing

arXiv

引用

arXiv 2020年

作者： Weber, Maurice Liu, Nana Li, Bo Zhang, Ce Zhao, Zhikuan Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai200240 China Department of Computer Science University of Illinois UrbanaIL61801 United States

Quantum machine learning models have the potential to offer speedups and better predictive accuracy compared to their classical counterparts. However, these quantum algorithms, like their classical counterparts, have been shown to also be vulnerable to input perturbations, in particular for classification problems. These can arise either from noisy implementations or, as a worst-case type of noise, adversarial attacks. In order to develop defence mechanisms and to better understand the reliability of these algorithms, it is crucial to understand their robustness properties in presence of natural noise sources or adversarial manipulation. From the observation that measurements involved in quantum classification algorithms are naturally probabilistic, we uncover and formalize a fundamental link between binary quantum hypothesis testing and provably robust quantum classification. This link leads to a tight robustness condition which puts constraints on the amount of noise a classifier can tolerate, independent of whether the noise source is natural or adversarial. Based on this result, we develop practical protocols to optimally certify robustness. Finally, since this is a robustness condition against worst-case types of noise, our result naturally extends to scenarios where the noise source is known. Thus, we also provide a framework to study the reliability of quantum classification protocols beyond the adversarial, worst-case noise scenarios. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum theory

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The Quantum Internet (Technical Version)

arXiv

引用

arXiv 2025年

作者： Rohde, Peter P. Huang, Zixin Ouyang, Yingkai Huang, He-Liang Su, Zu-En Devitt, Simon Ramakrishnan, Rohit Mantri, Atul Tan, Si-Hui Liu, Nana Harrison, Scott Radhakrishnan, Chandrashekar Brennen, Gavin K. Baragiola, Ben Q. Dowling, Jonathan P. Byrnes, Tim Munro, William J. Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Hearne Institute for Theoretical Physics Louisiana State University Baton Rouge United States School of Mathematical and Physical Sciences Macquarie University NSW2109 Australia School of Mathematical and Physical Sciences University of Sheffield SheffieldS3 7RH United Kingdom Henan Key Laboratory of Quantum Information and Cryptography Henan Zhengzhou450000 China Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science & Technology of China Hefei China CAS Centre for Excellence and Synergetic Innovation Centre in Quantum Information & Quantum Physics University of Science & Technology of China Hefei China CAS-Alibaba Quantum Computing Laboratory Shanghai China InstituteQ Aalto University Espoo02150 Finland Indian Institute of Science Bangalore India Singapore University of Technology & Design Singapore Centre for Quantum Technologies National University of Singapore Singapore Horizon Quantum Ireland 24 Fitzwilliam Place Dublin 2 D02 T296 Ireland Institute of Natural Sciences Shanghai Jiao Tong University Shanghai200240 China School of Mathematical Sciences Shanghai Jiao Tong University Shanghai200240 China Ministry of Education Key Laboratory in Scientific and Engineering Computing Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai200240 China Leibniz Institute for Research & Information in Education Frankfurt am Main Germany Department of Computer Science and Engineering NYU Shanghai 567 West Yangsi Road Shanghai200124 China Centre of Excellence in Engineered Quantum Systems Macquarie University NSW Australia Centre for Quantum Computation and Communication Technology School of Science RMIT University VIC

The desire to share and unite remote digital assets motivated the development of the classical internet, the enabler of the entire 21st century economy and our modern way of life. As we enter the quantum era, it is to be expected there will be a similar demand for networking quantum assets, motivating a global quantum internet for bringing together the world's quantum resources, leveraging off their exponential trajectory in capability. We present models for quantum networking, how they might be applied in the future, and the implications they will have. Socially, economically, politically and geostrategically, the upcoming era of quantum supremacy will be as significant for the 21st century as the transistor was for the 20th. The inherently different scaling in the computational power of quantum computers fundamentally changes the dynamics of how they will operate in the future. Given their high expected initial cost, a client/server model for outsourcing computation will be essential for enabling the accessibility and proliferation of this technology, and ensuring its economic viability. We therefore anticipate the emergence of cloud quantum computing, a model for outsourcing quantum computations to the network. We argue that economic efficiency will mandate that all future quantum computers be united into a single global virtual quantum computer, offering exponentially more power to all network participants than if they were to keep their resources to themselves. This model for the allocation of computational resources is uniquely quantum, with no classical analogue, completely altering the economic landscape for the future of computation. Given the sensitivity of much of the data to which future quantum computers are going to foreseeably be applied, protocols for encrypted quantum computation will be essential - the outsourcing of computations that neither an eavesdropper nor even the server performing the computation can spy upon. This will enable new models fo

关键词： Outsourcing

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Multi-reference Epstein-Nesbet perturbation theory with density matrix renormalization group reference wavefunction

arXiv

引用

arXiv 2019年

作者： Song, Yinxuan Cheng, Yifan Ma, Yingjin Ma, Haibo School of Chemistry and Chemical Engineering Nanjing University Nanjing210023 China Department of High Performance Computing Technology and Application Development Computer Network Information Center Chinese Academy of Sciences Beijing100190 China Center of Scientific Computing Applications & Research Chinese Academy of Sciences Beijing100190 China

The accurate electronic structure calculation for strongly correlated chemical systems requires an adequate description for both static and dynamic electron correlation, and is a persistent challenge for quantum chemistry. In order to account for static and dynamic electron correlations accurately and efficiently, in this work we propose a new method by integrating the density matrix renormalization group (DMRG) method and multi-reference second-order Epstein-Nesbet perturbation theory (ENPT2) with a selected configuration interaction (SCI) approximation. Compared to previous DMRG-based dynamic correlation methods, the DMRG-ENPT2 method extends the range of applicability, allowing us to efficiently calculate systems with very large active spaces beyond 30 orbitals. We demonstrate this by performing calculations on H2S with an active space of (16e, 15o), hexacene with an active space of (26e, 26o) and trinuclear Manganese cluster with an active space of (47e, 43o). Copyright © 2019, The Authors. All rights reserved.

关键词： Quantum chemistry

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Exploration of Differentiability in a Proton Computed Tomography Simulation Framework

arXiv

引用

arXiv 2022年

作者： Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Blühdorn, Johannes Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Eikeland, Viljar Feofilov, Gregory Garth, Christoph Gauger, Nicolas R. Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Kortus, Tobias Kusch, Lisa Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Pochampalli, Rohit Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Sagebaum, Max Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Zillien, Sebastian Chair for Scientific Computing University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab University of Kaiserslautern-Landau Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway University of Applied Sciences Worms Worms Germany Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary FSN Department ENEA Frascati Research Center Frascati00044 Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

Objective. Algorithmic differentiation (AD) can be a useful technique to numerically optimize design and algorithmic parameters by, and quantify uncertainties in, computer simulations. However, the effectiveness of AD depends on how "well-linearizable" the software is. In this study, we assess how promising derivative information of a typical proton computed tomography (pCT) scan computer simulation is for the aforementioned applications. Approach. This study is mainly based on numerical experiments, in which we repeatedly evaluate three representative computational steps with perturbed input values. We support our observations with a review of the algorithmic steps and arithmetic operations performed by the software, using debugging techniques. Main results. The model-based iterative reconstruction (MBIR) subprocedure (at the end of the software pipeline) and the Monte Carlo (MC) simulation (at the beginning) were piecewise differentiable. Jumps in the MBIR function arose from the discrete computation of the set of voxels intersected by a proton path. Jumps in the MC function likely arose from changes in the control flow that affect the amount of consumed random numbers. The tracking algorithm solves an inherently non-differentiable problem. Significance. The MC and MBIR codes are ready for the integration of AD, and further research on surrogate models for the tracking subprocedure is necessary. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

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Towards Neural Charged Particle Tracking in Digital Tracking Calorimeters with Reinforcement Learning

TechRxiv

引用

TechRxiv 2022年

作者： Kortus, Tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, Tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Genov, Georgi Grøttvik, Ola Helstrup, Håvard Igolkin, Sergey Keidel, Ralf Kobdaj, Chinorat Leonhardt, Viktor Mehendale, Shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, Thomas Pettersen, Helge Egil Seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter Santana, Joshua Schilling, Alexander Seco, Joao Songmoolnak, Arnon Sudár, Ákos Sølie, Jarle Rambo Tambave, Ganesh Tymchuk, Ihor Ullaland, Kjetil Varga-Kofarago, Monika Volz, Lennart Wagner, Boris Wendzel, Steffen Wiebel, Alexander Xiao, RenZheng Yang, Shiming Yokoyama, Hiroki Zillien, Sebastian University of Applied Sciences Worms Worms67549 Germany Chair for Scientific Computing TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Bhubaneswar India Biophysics GSI Helmholtz Center for Heavy Ion Research GmbH Darmstadt Germany Department of Medical Physics and Biomedical Engineering University College London London United Kingdom College of Mechanical & Power Engineering China Three Gorges University Yichang China

We propose a novel technique for reconstructing charged particles in digital tracking calorimeters using reinforcement learning aiming to benefit from the rapid progress and success of neural network architectures without the dependency on simulated or manually-labeled data. Here we optimize by trial-and-error a behavior policy acting as an approximation to the full combinatorial optimization problem, maximizing the physical plausibility of sampled trajectories. In modern processing pipelines used in high energy physics and related applications, tracking plays an essential role allowing to identify and follow charged particle trajectories traversing particle detectors. Due to the high multiplicity of charged particles and their physical interactions, randomly deflecting the particles, the reconstruction is a challenging undertaking, requiring fast, accurate and robust algorithms. Our approach works on graph-structured data, capturing track hypotheses through edge connections between particles in the detector layers. We demonstrate in a comprehensive study on simulated data for a particle detector used for proton computed tomography, the high potential as well as the competitiveness of our approach compared to a heuristic search algorithm and a model trained on ground truth. Finally, we point out limitations of our approach, guiding towards a robust foundation for further development of reinforcement learning based tracking. © 2022, CC BY.

关键词： Reinforcement learning

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