scholarly journals Improved deep learning techniques in gravitational-wave data analysis

2021 ◽  
Vol 103 (2) ◽  
Author(s):  
Heming Xia ◽  
Lijing Shao ◽  
Junjie Zhao ◽  
Zhoujian Cao
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Gravitational Wave ◽  
Wave Data ◽  
Learning Techniques

scholarly journals Noise reduction in gravitational-wave data via deep learning

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Rich Ormiston ◽  
Tri Nguyen ◽  
Michael Coughlin ◽  
Rana X. Adhikari ◽  
Erik Katsavounidis
Keyword(s):  
Deep Learning ◽  
Gravitational Wave ◽  
Noise Reduction ◽  
Wave Data

scholarly journals Maximum Entropy for Gravitational Wave Data Analysis: Inferring the Physical Parameters of Core‐Collapse Supernovae

2008 ◽  
Vol 678 (2) ◽  
pp. 1142-1157 ◽  
Author(s):  
T. Z. Summerscales ◽  
Adam Burrows ◽  
Lee Samuel Finn ◽  
Christian D. Ott
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Maximum Entropy ◽  
Physical Parameters ◽  
Core Collapse ◽  
Wave Data

scholarly journals Model waveform accuracy standards for gravitational wave data analysis

2008 ◽  
Vol 78 (12) ◽  
Author(s):  
Lee Lindblom ◽  
Benjamin J. Owen ◽  
Duncan A. Brown
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Wave Data

scholarly journals Deep Learning Application to Surface Properties Retrieval Using TIR Measurements: A Fast Forward/Reverse Scheme to Deal with Big Data Analysis from New Satellite Generations

2021 ◽  
Vol 13 (24) ◽  
pp. 5003
Author(s):  
Elisa Castelli ◽  
Enzo Papandrea ◽  
Alessio Di Roma ◽  
Ilaria Bloise ◽  
Mattia Varile ◽  
...  
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
State Of The Art ◽  
Spectral Response ◽  
High Spectral Resolution ◽  
Learning Techniques

In recent years, technology advancement has led to an enormous increase in the amount of satellite data. The availability of huge datasets of remote sensing measurements to be processed, and the increasing need for near-real-time data analysis for operational uses, has fostered the development of fast, efficient-retrieval algorithms. Deep learning techniques were recently applied to satellite data for retrievals of target quantities. Forward models (FM) are a fundamental part of retrieval code development and mission design, as well. Despite this, the application of deep learning techniques to radiative transfer simulations is still underexplored. The DeepLIM project, described in this work, aimed at testing the feasibility of the application of deep learning techniques at the design of the retrieval chain of an upcoming satellite mission. The Land Surface Temperature Mission (LSTM) is a candidate for Sentinel 9 and has, as the main target, the need, for the agricultural community, to improve sustainable productivity. To do this, the mission will carry a thermal infrared sensor to retrieve land-surface temperature and evapotranspiration rate. The LSTM land-surface temperature retrieval chain is used as a benchmark to test the deep learning performances when applied to Earth observation studies. Starting from aircraft campaign data and state-of-the-art FM simulations with the DART model, deep learning techniques are used to generate new spectral features. Their statistical behavior is compared to the original technique to test the generation performances. Then, the high spectral resolution simulations are convolved with LSTM spectral response functions to obtain the radiance in the LSTM spectral channels. Simulated observations are analyzed using two state-of-the-art retrieval codes and deep learning-based algorithms. The performances of deep learning algorithms show promising results for both the production of simulated spectra and target parameters retrievals, one of the main advances being the reduction in computational costs.

scholarly journals OctApps: a library of Octave functions for continuous gravitational-wave data analysis

2018 ◽  
Vol 3 (26) ◽  
pp. 707 ◽  
Author(s):  
Karl Wette ◽  
Reinhard Prix ◽  
David Keitel ◽  
Matthew Pitkin ◽  
Christoph Dreissigacker ◽  
...  
Keyword(s):  
Data Analysis ◽  
Gravitational Wave ◽  
Wave Data
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