Gravitational wave astrophysics, data analysis and multimessenger astronomy

نویسندگان

  • Hyung Mok Lee
  • Eric-Olivier Le Bigot
  • Zhihui Du
  • ZhangXi Lin
  • Xiangyu Guo
  • LinQing Wen
  • Khun Sang Phukon
  • Vihan Pandey
  • Sukanta Bose
  • Xi-Long Fan
  • Martin Hendry
چکیده

1Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea; 2Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China; 3Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China; 4School of Physics, The University of Western Australia, M468, 35 Stirling Hwy, Crawley, WA 6009, Australia; 5Department of Physics, IIT Kanpur, Kanpur 208 016, India; 6Inter-University Centre for Astronomy and Astrophysics, Post Bag 4, Ganeshkhind, Pune 411 007, India; 7Department of Physics & Astronomy, Washington State University, 1245 Webster, Pullman, WA 99164-2814, USA; 8School of Physics and Electronics Information, Hubei University of Education, Wuhan 430205, China; 9SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduce Deep Filtering, a new highly scalable method for end-to-end time-series sig...

متن کامل

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent field of science, we pioneered the use of deep learning with convolutional neural networks, th...

متن کامل

Multimessenger astronomy with pulsar timing and X-ray observations of massive black hole binaries

In the decade of the dawn of gravitational wave astronomy, the concept of multimessenger astronomy, combining gravitational wave signals to conventional electromagnetic observation, has attracted the attention of the astrophysical community. So far, most of the effort has been focused on groundand space-based laser interferometer sources, with little attention devoted to the ongoing and upcomin...

متن کامل

Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data

The recent Nobel-prize-winning detections of gravitational waves from merging black holes and the subsequent detection of the collision of two neutron stars in coincidence with electromagnetic observations have inaugurated a new era of multimessenger astrophysics. To enhance the scope of this emergent science, the use of deep convolutional neural networks were proposed for the detection and cha...

متن کامل

A Decision between Bayesian and Frequentist Upper Limit in Analyzing Continuous Gravitational Waves

Given the sensitivity of current ground-based Gravitational Wave (GW) detectors, any continuous-wave signal we can realistically expect will be at a level or below the background noise. Hence, any data analysis of detector data will need to rely on statistical techniques to separate the signal from the noise. While with the current sensitivity of our detectors we do not expect to detect any tru...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • CoRR

دوره abs/1602.05573  شماره 

صفحات  -

تاریخ انتشار 2016