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Open Astronomy

formerly Baltic Astronomy

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IMPACT FACTOR 2017 (Baltic Astronomy): 0.417
5-year IMPACT FACTOR (Baltic Astronomy): 0.486

CiteScore 2017: 0.16

SCImago Journal Rank (SJR) 2017: 0.131
Source Normalized Impact per Paper (SNIP) 2017: 0.109

Open Access
Online
ISSN
2543-6376
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Volume 26, Issue 1

Issues

The applications of deep neural networks to sdBV classification

Thomas M. Boudreaux
Published Online: 2017-12-29 | DOI: https://doi.org/10.1515/astro-D-17-0450

Abstract

With several new large-scale surveys on the horizon, including LSST, TESS, ZTF, and Evryscope, faster and more accurate analysis methods will be required to adequately process the enormous amount of data produced. Deep learning, used in industry for years now, allows for advanced feature detection in minimally prepared datasets at very high speeds; however, despite the advantages of this method, its application to astrophysics has not yet been extensively explored. This dearth may be due to a lack of training data available to researchers. Here we generate synthetic data loosely mimicking the properties of acoustic mode pulsating stars and we show that two separate paradigms of deep learning - the Artificial Neural Network And the Convolutional Neural Network - can both be used to classify this synthetic data effectively. And that additionally this classification can be performed at relatively high levels of accuracy with minimal time spent adjusting network hyperparameters.

Keywords : Deep Learning; sdBV

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About the article

Received: 2017-09-29

Accepted: 2017-11-30

Published Online: 2017-12-29

Published in Print: 2017-12-20


Citation Information: Open Astronomy, Volume 26, Issue 1, Pages 258–269, ISSN (Online) 2543-6376, DOI: https://doi.org/10.1515/astro-D-17-0450.

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© 2018. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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