The applications of deep neural networks to sdBV classification
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 i...
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| Format: | Article |
| Language: | English |
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De Gruyter
2017-12-01
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| Series: | Open Astronomy |
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| Online Access: | https://doi.org/10.1515/astro-D-17-0450 |
| _version_ | 1818650545091510272 |
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| author | Boudreaux Thomas M. |
| author_facet | Boudreaux Thomas M. |
| author_sort | Boudreaux Thomas M. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T01:51:55Z |
| format | Article |
| id | doaj.art-d52e126c1f1e4131a0adc7164c7a2e8d |
| institution | Directory Open Access Journal |
| issn | 2543-6376 |
| language | English |
| last_indexed | 2024-12-17T01:51:55Z |
| publishDate | 2017-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Open Astronomy |
| spelling | doaj.art-d52e126c1f1e4131a0adc7164c7a2e8d2022-12-21T22:08:02ZengDe GruyterOpen Astronomy2543-63762017-12-0126125826910.1515/astro-D-17-0450astro-D-17-0450The applications of deep neural networks to sdBV classificationBoudreaux Thomas M.0Department of Physics, High Point University, One University Parkway, High Point, NC 27268, USAWith 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.https://doi.org/10.1515/astro-D-17-0450deep learningsdbv |
| spellingShingle | Boudreaux Thomas M. The applications of deep neural networks to sdBV classification Open Astronomy deep learning sdbv |
| title | The applications of deep neural networks to sdBV classification |
| title_full | The applications of deep neural networks to sdBV classification |
| title_fullStr | The applications of deep neural networks to sdBV classification |
| title_full_unstemmed | The applications of deep neural networks to sdBV classification |
| title_short | The applications of deep neural networks to sdBV classification |
| title_sort | applications of deep neural networks to sdbv classification |
| topic | deep learning sdbv |
| url | https://doi.org/10.1515/astro-D-17-0450 |
| work_keys_str_mv | AT boudreauxthomasm theapplicationsofdeepneuralnetworkstosdbvclassification AT boudreauxthomasm applicationsofdeepneuralnetworkstosdbvclassification |