Abstract
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.
Highlights
The traditional method to address spectral classification of stars is to combine their photometric and spectroscopic data together
A huge number of spectra are obtained with the emergence of sky survey telescopes, such as Sloan Digital Sky Survey (SDSS) [5,6] and Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) [7,8]
From a qualitative and quantitative perspective, we proved the high quality of the generated spectra and the effectiveness and robustness of the Adversarial AutoEncoder (AAE)
Summary
The traditional method to address spectral classification of stars is to combine their photometric and spectroscopic data together. The most commonly used Harvard stellar spectral classification system was proposed by the Harvard University Observatory in the late 19th century [1]. In accordance with the order of the surface temperature of the star, the system divides the stellar spectra into O, B, A, F, G, K, M, and other types [2]. M dwarfs are the most common stars in the Galaxy [3] and are characterized by low brightness, small diameter and mass, and a surface temperature around or lower than 3500 K. A huge number of spectra are obtained with the emergence of sky survey telescopes, such as Sloan Digital Sky Survey (SDSS) [5,6] and Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) [7,8]
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