Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010
Seeing is a key factor affecting the image quality of astronomical observations and can be quantitatively described by the Fried parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi...
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| author | Xing Hu Shangbin Yang Tengfei Song Xingming Bao Wenjun Sun Yuanyong Deng Yu Liu Mingyu Zhao |
| author_facet | Xing Hu Shangbin Yang Tengfei Song Xingming Bao Wenjun Sun Yuanyong Deng Yu Liu Mingyu Zhao |
| author_sort | Xing Hu |
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| description | Seeing is a key factor affecting the image quality of astronomical observations and can be quantitatively described by the Fried parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula>. The larger the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> value (in unit of cm), the better the seeing conditions. Currently, daytime seeing measurements are primarily conducted using the Solar Differential Image Motion Monitor (SDIMM) or the spectral ratio method. In this work, we propose a neural network model for estimating daytime <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula>. The experimental results of the training set and the test set show that this model can currently estimate <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> with an accuracy exceeding <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Using this model, we estimate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> of the Huairou Solar Observing Station (HSOS) in 22 consecutive years from 1989 to 2010. The median <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> of HSOS in 22 consecutive years was around 2.5 cm, and the best seeing condition was in April and September of one year. This result confirmed the long-term stability of seeing conditions. In addition, we conducted an error analysis comparing the seeing measured by SDIMM and the results obtained by the spectral ratio method both under domeless and domed conditions. The results indicate a significant correlation between the SDIMM results and the spectral ratio method results, with first-order fitting coefficients of 2.2 and 2.9, respectively. |
| format | Article |
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| spelling | doaj-art-6084fecddf914c4ab01a5ef87f6206cc2025-08-20T02:21:54ZengMDPI AGUniverse2218-19972025-05-0111616910.3390/universe11060169Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010Xing Hu0Shangbin Yang1Tengfei Song2Xingming Bao3Wenjun Sun4Yuanyong Deng5Yu Liu6Mingyu Zhao7State Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaState Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Solar Activity and Space Weather, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Physical Science and Technology, Southwest Jiaotong University, Chengdu 611756, ChinaYunnan Observatories, Chinese Academy of Sciences, Kunming 650217, ChinaSeeing is a key factor affecting the image quality of astronomical observations and can be quantitatively described by the Fried parameter <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula>. The larger the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> value (in unit of cm), the better the seeing conditions. Currently, daytime seeing measurements are primarily conducted using the Solar Differential Image Motion Monitor (SDIMM) or the spectral ratio method. In this work, we propose a neural network model for estimating daytime <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula>. The experimental results of the training set and the test set show that this model can currently estimate <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> with an accuracy exceeding <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula>. Using this model, we estimate the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> of the Huairou Solar Observing Station (HSOS) in 22 consecutive years from 1989 to 2010. The median <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>r</mi><mn>0</mn></msub></semantics></math></inline-formula> of HSOS in 22 consecutive years was around 2.5 cm, and the best seeing condition was in April and September of one year. This result confirmed the long-term stability of seeing conditions. In addition, we conducted an error analysis comparing the seeing measured by SDIMM and the results obtained by the spectral ratio method both under domeless and domed conditions. The results indicate a significant correlation between the SDIMM results and the spectral ratio method results, with first-order fitting coefficients of 2.2 and 2.9, respectively.https://www.mdpi.com/2218-1997/11/6/169day-time seeingneural networkestimationerror analysis |
| spellingShingle | Xing Hu Shangbin Yang Tengfei Song Xingming Bao Wenjun Sun Yuanyong Deng Yu Liu Mingyu Zhao Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 Universe day-time seeing neural network estimation error analysis |
| title | Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 |
| title_full | Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 |
| title_fullStr | Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 |
| title_full_unstemmed | Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 |
| title_short | Estimation of Day-Time Seeing Changes at Huairou Solar Observing Station Based on Neural Networks from 1989 to 2010 |
| title_sort | estimation of day time seeing changes at huairou solar observing station based on neural networks from 1989 to 2010 |
| topic | day-time seeing neural network estimation error analysis |
| url | https://www.mdpi.com/2218-1997/11/6/169 |
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