Extension of M Dwarf Spectra Based on Adversarial AutoEncoder
M dwarfs are main sequence stars and they exist in all stages of galaxy evolution. As the living fossils of cosmic evolution, the study of M dwarfs is of great significance to the understanding of stars and the stellar populations of the Milky Way. Previously, M dwarf research was limited due to ins...
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| Format: | Article |
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MDPI AG
2021-08-01
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| Series: | Universe |
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| Online Access: | https://www.mdpi.com/2218-1997/7/9/326 |
| _version_ | 1797517079294771200 |
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| author | Jiyu Wei Xingzhu Wang Bo Li Yuze Chen Bin Jiang |
| author_facet | Jiyu Wei Xingzhu Wang Bo Li Yuze Chen Bin Jiang |
| author_sort | Jiyu Wei |
| collection | DOAJ |
| description | M dwarfs are main sequence stars and they exist in all stages of galaxy evolution. As the living fossils of cosmic evolution, the study of M dwarfs is of great significance to the understanding of stars and the stellar populations of the Milky Way. Previously, M dwarf research was limited due to insufficient spectroscopic spectra. Recently, the data volume of M dwarfs was greatly increased with the launch of large sky survey telescopes such as Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopy Telescope. However, the spectra of M dwarfs mainly concentrate in the subtypes of M0–M4, and the number of M5–M9 is still relatively limited. With the continuous development of machine learning, the generative model was improved and provides methods to solve the shortage of specified training samples. In this paper, the Adversarial AutoEncoder is proposed and implemented to solve this problem. Adversarial AutoEncoder is a probabilistic AutoEncoder that uses the Generative Adversarial Nets to generate data by matching the posterior of the hidden code vector of the original data extracted by the AutoEncoder with a prior distribution. Matching the posterior to the prior ensures each part of prior space generated results in meaningful data. To verify the quality of the generated spectra data, we performed qualitative and quantitative verification. The experimental results indicate the generation spectra data enhance the measured spectra data and have scientific applicability. |
| first_indexed | 2024-03-10T07:09:48Z |
| format | Article |
| id | doaj.art-906990d305b14f61a44c5a1df5d7d1f4 |
| institution | Directory Open Access Journal |
| issn | 2218-1997 |
| language | English |
| last_indexed | 2024-03-10T07:09:48Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Universe |
| spelling | doaj.art-906990d305b14f61a44c5a1df5d7d1f42023-11-22T15:32:54ZengMDPI AGUniverse2218-19972021-08-017932610.3390/universe7090326Extension of M Dwarf Spectra Based on Adversarial AutoEncoderJiyu Wei0Xingzhu Wang1Bo Li2Yuze Chen3Bin Jiang4School of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, ChinaSchool of Mechanical, Electrical & Information Engineering, Shandong University, Weihai 264209, ChinaM dwarfs are main sequence stars and they exist in all stages of galaxy evolution. As the living fossils of cosmic evolution, the study of M dwarfs is of great significance to the understanding of stars and the stellar populations of the Milky Way. Previously, M dwarf research was limited due to insufficient spectroscopic spectra. Recently, the data volume of M dwarfs was greatly increased with the launch of large sky survey telescopes such as Sloan Digital Sky Survey and Large Sky Area Multi-Object Fiber Spectroscopy Telescope. However, the spectra of M dwarfs mainly concentrate in the subtypes of M0–M4, and the number of M5–M9 is still relatively limited. With the continuous development of machine learning, the generative model was improved and provides methods to solve the shortage of specified training samples. In this paper, the Adversarial AutoEncoder is proposed and implemented to solve this problem. Adversarial AutoEncoder is a probabilistic AutoEncoder that uses the Generative Adversarial Nets to generate data by matching the posterior of the hidden code vector of the original data extracted by the AutoEncoder with a prior distribution. Matching the posterior to the prior ensures each part of prior space generated results in meaningful data. To verify the quality of the generated spectra data, we performed qualitative and quantitative verification. The experimental results indicate the generation spectra data enhance the measured spectra data and have scientific applicability.https://www.mdpi.com/2218-1997/7/9/326M-type dwarfsadversarial AutoEncoderspectral data generationsky survey |
| spellingShingle | Jiyu Wei Xingzhu Wang Bo Li Yuze Chen Bin Jiang Extension of M Dwarf Spectra Based on Adversarial AutoEncoder Universe M-type dwarfs adversarial AutoEncoder spectral data generation sky survey |
| title | Extension of M Dwarf Spectra Based on Adversarial AutoEncoder |
| title_full | Extension of M Dwarf Spectra Based on Adversarial AutoEncoder |
| title_fullStr | Extension of M Dwarf Spectra Based on Adversarial AutoEncoder |
| title_full_unstemmed | Extension of M Dwarf Spectra Based on Adversarial AutoEncoder |
| title_short | Extension of M Dwarf Spectra Based on Adversarial AutoEncoder |
| title_sort | extension of m dwarf spectra based on adversarial autoencoder |
| topic | M-type dwarfs adversarial AutoEncoder spectral data generation sky survey |
| url | https://www.mdpi.com/2218-1997/7/9/326 |
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