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Daniel George

Researcher at University of Illinois at Urbana–Champaign

Publications -  14
Citations -  11043

Daniel George is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: LIGO & Gravitational wave. The author has an hindex of 12, co-authored 13 publications receiving 9152 citations. Previous affiliations of Daniel George include Indian Institute of Technology Bombay.

Papers
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GW170817: observation of gravitational waves from a binary neutron star inspiral

B. P. Abbott, +1134 more
TL;DR: The association of GRB 170817A, detected by Fermi-GBM 1.7 s after the coalescence, corroborates the hypothesis of a neutron star merger and provides the first direct evidence of a link between these mergers and short γ-ray bursts.
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GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2

B. P. Abbott, +1065 more
TL;DR: The magnitude of modifications to the gravitational-wave dispersion relation is constrain, the graviton mass is bound to m_{g}≤7.7×10^{-23} eV/c^{2} and null tests of general relativity are performed, finding that GW170104 is consistent with general relativity.
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Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data

TL;DR: In this paper, an extension of Deep Filtering using real data from LIGO was presented for both detection and parameter estimation of gravitational waves from binary black hole mergers using continuous data streams from multiple LigO detectors.
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Deep Neural Networks to Enable Real-time Multimessenger Astrophysics

TL;DR: Deep Filtering is introduced, a new highly scalable method for end-to-end time-series signal processing, based on a system of two deep convolutional neural networks, which is designed for classification and regression to rapidly detect and estimate parameters of signals in highly noisy time- series data streams.
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Classification and unsupervised clustering of LIGO data with Deep Transfer Learning

TL;DR: The first application of Deep Transfer Learning for glitch classification is presented, showing that knowledge from deep learning algorithms trained for real-world object recognition can be transferred for classifying glitches in time-series based on their spectrogram images.