Deep Learning Networks and Gravitational Wave Signal Recognization

Document #:

JGW-G1910653-v1

Document type:

G

Submitted by:

He Wang

Updated by:

He Wang

Document Created:

24 Aug 2019, 14:29

Contents Revised:

24 Aug 2019, 14:29

DB Info Revised:

24 Aug 2019, 14:29

Abstract:

Deep learning is a neural-inspired pattern recognition technique that has been shown to be as effective as conventional signal processing and has considerable potential to identify gravitational-wave signals. In this talk, I will first review some works on the detection and characterization of gravitational-wave signals, then present our related progress about the effect of deep neural networks on the recognition and identifying generalization properties of gravitational waves. After that, based on a proposed idea from matched-filtering method, we design a brand new architecture (MF-CNN) and . At last, I will explain some details of our experiments and the main properties on the real LIGO recordings.

Files in Document:

• HTML version (with animation) (23th_kagra_f2f.html, 1.5 MB)
• PDF version (without animation) (23th_kagra_f2f.pdf, 3.1 MB)

Topics:

• Activity:Data Analysis
• Management:Collaboration meeting:KAGRA F2F
• Detector:Data Analysis

Authors:

• He Wang

Keywords:

Data-Analysis Machine-Learning Deep-Learning