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Search for anisotropic gravitational-wave backgrounds using data from Advanced LIGO and Advanced Virgo’s first three observing runs

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Physical Review D 2021年第2期104卷 022005-022005页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic R. L. Byer L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S.Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illinois

We report results from searches for anisotropic stochastic gravitational-wave backgrounds using data from the first three observing runs of the Advanced LIGO and Advanced Virgo detectors. For the first time, we include Virgo data in our analysis and run our search with a new efficient pipeline called pystoch on data folded over one sidereal day. We use gravitational-wave radiometry (broadband and narrow band) to produce sky maps of stochastic gravitational-wave backgrounds and to search for gravitational waves from point sources. A spherical harmonic decomposition method is employed to look for gravitational-wave emission from spatially-extended sources. Neither technique found evidence of gravitational-wave signals. Hence we derive 95% confidence-level upper limit sky maps on the gravitational-wave energy flux from broadband point sources, ranging from Fα,Θ<(0.013–7.6)×10−8 erg cm−2 s−1 Hz−1, and on the (normalized) gravitational-wave energy density spectrum from extended sources, ranging from Ωα,Θ<(0.57–9.3)×10−9 sr−1, depending on direction (Θ) and spectral index (α). These limits improve upon previous limits by factors of 2.9–3.5. We also set 95% confidence level upper limits on the frequency-dependent strain amplitudes of quasimonochromatic gravitational waves coming from three interesting targets, Scorpius X-1, SN 1987A and the Galactic Center, with best upper limits range from h0<(1.7–2.1)×10−25, a factor of ≥2.0 improvement compared to previous stochastic radiometer searches.

关键词： Experimental studies of gravity General relativity Gravitational waves

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Mars Entry Trajectory Tracking Via Constrained Multi-Model Predictive control 33

Mars Entry Trajectory Tracking Via Constrained Multi-Model P...

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第三十三届中国控制会议

作者： WU Chao LI Shihua YANG Jun GUO Lei School of Automation Southeast University & Key Laboratory of Measurement and Control of Complex Systems of EngineeringMinistry of Education School of Automation Science and Electrical Engineering Beihang University & Institute of Instrument Science and Opto-Electronics Engineering

ISBN: (纸本)9781479947249

Tracking law designed for future Mars entry missions is investigated in this article. For precision landing, the nonlinear entry dynamics, complex uncertainties and input saturation constraints are unavoidable problems. Facing these challenges, the Mars entry trajectory tracking scheme via constrained multi-model predictive control(CMPC) is employed. The CMPC is made up of some constrained predictive control(CPC) in different time domain during the Mars entry mission. Each constrained predictive controller consists of a linearized prediction model(obtained by linearizing at different operating point), feedback correction for active model mismatch rejection caused by the complex uncertainties, and constrained rolling optimization for a smooth control input under a saturation constraint. Monte Carlo simulations demonstrate the effectiveness and excellence of the proposed method under the saturation constraint of input and uncertainties of initial state and aerodynamic parameters such as atmospheric density, ballistic coefficient and lift-to-drag ratio.

关键词： Mars Entry,Trajectory Tracking,Constrained Multi-Model Predictive control,Monte Carlo Simulation

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On the computational study of artificial fish swarm algorithm and its improvement

On the computational study of artificial fish swarm algorith...

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2014 International Conference on Manufacturing Technology and Electronics Applications, ICMTEA 2014

作者： Peng, Pei-Zhen Yu, Yi Wang, Zhao-Jia Jiang, Min Key Laboratory of Measurement and Control of Complex Engineering of Ministry of Education School of Automation Southeast University NanjingJiangsu China

ISBN: (纸本)9783038353287

Artificial Fish Swarm Algorithm (AFSA) since 2002 has been proposed by Dr. Li Xiao-lei more than ten years, and has been widely used in various engineering fields. However, since a lot of comprehensive standard running tests has not yet been made with the algorithm, it has not yet been unanimously recognized by the international academic community. By 34 Benchmark Functions tested with AFSA, the result evaluation for functions that are applicable and not applicable by AFSA is summarized. Also in order to overcome the drawbacks of Global Artificial Fish Swarm Algorithm (GAFSA) such as slow convergence and low precision, a modified GAFSA(MS_GAFSA) is proposed. Combined with GAFSA and Modified Simplex, the algorithm can improve the convergence speed and precision of optimization. When GAFSA converges to the global optimum nearby, a simplex is constructed and the algorithm switches to Modified Simplex method which will continue to optimize until a certain stop condition is satisfied. Take the best point of simplex vertex at this time as the optimal value. The computational results on 34 Benchmark functions show that MS_GAFSA does improve in optimizing accuracy and convergence speed. © (2014) Trans Tech Publications, Switzerland.

关键词： Global optimization

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Adaptive fuzzy control of output-constrained switched nonlinear systems

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ICIC Express Letters 2015年第12期9卷 3355-3362页

作者： Niu, Ben Liu, Yanli Shen, Mouquan School of Control Science and Engineering Dalian University of Technology No. 2 Linggong Road Ganjingzi District Dalian China College of Mathematics and Physics Bohai University No. 19 Keji Road New Songshan District Jinzhou China Automation Research Institute Bohai University No. 19 Keji Road New Songshan District Jinzhou China College of Electrical Engineering and Control Science Nanjing Technology University Nanjing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

In this paper, the problem of adaptive fuzzy tracking control is considered of uncertain switched nonlinear systems with an output constraint. First, a barrier Lyapunov function (BLF) is employed to deal with the output constraint. Then fuzzy logic systems are used to approximate the unknown nonlinearities and backstepping technique is employed to construct an adaptive fuzzy controller. The designed controller guarantees all the closed-loop signals remain bounded while the system output converges to a small neighborhood of the desired reference signal without violating the constraint. An example is given to demonstrate the validity of the developed result. © 2015 ICIC International.

关键词： Lyapunov functions

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Pt-Containing Ag_2S-Noble Metal Nanocomposites as Highly Active Electrocatalysts for the Oxidation of Formic Acid

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Nano-Micro Letters 2014年第3期6卷 252-257页

作者： Hui Liu Yan Feng Hongbin Cao Jun Yang State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Research Centre for Process Pollution Control Institute of Process Engineering Chinese Academy of Sciences

Nanocomposites with synergistic effect are of great interest for their enhanced properties in a given application. Herein, we reported the high catalytic activity of Pt-containing Ag2S-noble metal nanocomposites in formic acid oxidation, which is a key reaction in direct formic acid fuel cell. The electrochemical measurements including voltammograms and chronoamperograms are used to characterize the catalytic property of Pt-containing nanocomposites for the oxidation of formic acid. In view of the limited literatures on using nanocomposites consisting of semiconductor and noble metals for catalyzing the reactions of polymer electrolyte membrane-based fuel cells, this study provides a helpful exploration for expanding the application of semiconductor-noble metal nanocomposites.

关键词： Nanocomposites Synergistic effect Formic acid oxidation Direct formic acid fuel cell

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High-level spatial modeling in convolutional neural network with application to pedestrian detection

High-level spatial modeling in convolutional neural network ...

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Canadian Conference on Electrical and Computer engineering (CCECE)

作者： Feng Liu Yongzhen Huang Wankou Yang Changyin Sun School of Automation. Southeast University Nanjing China Key Lab of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China Key Lab of Child Development and Learning Science of Ministry of Education. Southeast University Nanjing China

ISBN: (纸本)9781479958306

Convolutional neural network (CNN) has achieved great success in many vision tasks. A key to this success is its ability to powerful automatically learns both high-level and low-level features. In general, low-level features have a small size of receptive fields and appear multiple times in different locations of objects, while high-level semantic features have a relatively large size of receptive fields and only appear once in a specific location of objects. However, traditional CNN treats these two kinds of features in the same manner, i.e., learning them by the convolution operation, which can be approximately considered as cumulating the probabilities that a feature appears in different locations. This strategy is reasonable for low-level features but not for high-level semantic ones, especially in the case of pedestrian detection, where a local feature can be shared by different locations but a semantic part, e.g., a head, only appears once for a human. To jointly model the spatial structure and appearance of high-level semantic features, we propose a new module to learn spatially weighted max pooling in CNN. The proposed method is evaluated on several pedestrian detection databases and the experimental results show that it achieves much better performance than traditional CNN.

关键词： Convolution Semantics Kernel Deformable models Computational modeling Feature extraction Training

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Novel Embedded System Design and Implementation for Automatic Platelet Aggregometer 33

Novel Embedded System Design and Implementation for Automati...

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第三十三届中国控制会议

作者： JU Ben MA Xudong FANG Fang XU Xin DONG Ziquan SINNOWA Medical Science & Technology Co. LTD School of Automation Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast University

ISBN: (纸本)9781479947249

The platelet aggregation function analysis is applied in clinical research and application more and more *** this paper,an automatic double-channel platelet analysis system PL12 is designed and *** system framework is based on embedded processor ARM and DSP architecture,in which a signal modulation circuit is *** an analog to digital conversion scheme is proposed and realized for measuring weak pulse signals of blood *** principle of control system and management software is also *** validate the high efficiency,precision and stability of the system.

关键词： double-channel,platelet aggregation,signal acquisition,embedded systems

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Attitude synchronization for multiple flexible spacecraft based on non-smooth control 19

Attitude synchronization for multiple flexible spacecraft ba...

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19th IFAC World Congress on International Federation of Automatic control, IFAC 2014

作者： Du, Haibo He, Yigang Li, Shihua Cheng, Yingying Chen, Xisong School of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing210096 China

ISBN: (纸本)9783902823625

This paper considers the problem of attitude synchronization for a group of flexible spacecraft based on distributed attitude cooperative control strategy. Based on the backstepping design, non-smooth control, and the neighbor-based design rule, a distributed attitude control law is constructed step by step. Under the proposed control law, it is shown that the attitude synchronization is achieved asymptotically and the induced vibration by flexible appendages is simultaneously suppressed. © IFAC.

关键词： control theory

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Continuous Travel Time Prediction for Transit Signal Priority Based on a Deep Network

Continuous Travel Time Prediction for Transit Signal Priorit...

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International Conference on Intelligent Transportation

作者： Xiong Gang Wenwen Kang Feiyue Wang Fenghua Zhu Yisheng Lv Xisong Dong Jukka Riekki Susanna Pirttikangas State Key Lab. of Manage. & Control for Complex Syst. Institute of Automation Chinese Academy of Sciences Beijing Beijing CN The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Cloud Computing Center Chinese Academy of Sciences Dongguan CHINA Faculty of Information Technology and Electrical Engineering University of Oulu Finland

It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.

关键词： Vehicles Predictive models Object oriented modeling Training Artificial neural networks Data models

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Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

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Physical Review D 2021年第2期104卷 022004-022004页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic R. L. Byer L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan California State University Fullerton Fullerton California 92831 USA NCSA University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Università di Napoli “Federico II” Complesso Universitario

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density ΩGW≤5.8×10−9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; ΩGW(f)≤3.4×10−9 at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and ΩGW(f)≤3.9×10−10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2/3, and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z≳2

关键词： Gravitational wave detection

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