分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The development of modern technology extends human presence beyond cislunar space and onto other planets, which presents an urgent need for high-capacity, long-distance and interplanetary communication. Communication using photons as carriers has a high channel capacity, but the optical diffraction limit in deep space leads to inevitable huge geometric loss, setting an insurmountable transmission distance for existing optical communication technologies. Here, we propose and experimentally demonstrate a photon-inter-correlation optical communication (PICOC) against an ultra-high channel loss. We treat light as a stream of photons, and retrieve the additional information of internal correlation and photon statistics globally from extremely weak pulse sequences. We successfully manage to build high-fidelity communication channel with a loss up to 160dB by separating a single-photon signal embedded in a noise ten times higher. With only commercially available telescopes, PICOC allows establishment of communication links from Mars to Earth communication using a milliwatt laser, and from the edge of the solar system to Earth using a few watts laser.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Variable stars play a key role in understanding the Milky Way and the universe. The era of astronomical big data presents new challenges for quick identification of interesting and important variable stars. Accurately estimating the periods is the most important step to distinguish different types of variable stars. Here, we propose a new method of determining the variability periods. By combining the statistical parameters of the light curves, the colors of the variables, the window function and the GLS algorithm, the aperiodic variables are excluded and the periodic variables are divided into eclipsing binaries and NEB variables (other types of periodic variable stars other than eclipsing binaries), the periods of the two main types of variables are derived. We construct a random forest classifier based on 241,154 periodic variables from the ASAS-SN and OGLE datasets of variables. The random forest classifier is trained on 17 features, among which 11 are extracted from the light curves and 6 are from the Gaia Early DR3, ALLWISE and 2MASS catalogs. The variables are classified into 7 superclasses and 17 subclasses. In comparison with the ASAS-SN and OGLE catalogs, the classification accuracy is generally above approximately 82% and the period accuracy is 70%-99%. To further test the reliability of the new method and classifier, we compare our results with the results of Chen et al. (2020) for ZTF DR2. The classification accuracy is generally above 70%. The period accuracy of the EW and SR variables is 50% and 53%, respectively. And the period accuracy of other types of variables is 65%-98%.
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