The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(F...The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(FAST).Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes.Over a ten-second period,the phases shift by 18°while the amplitudes fluctuate by 6 mK.Thus,we developed the fast Fourier transform(FFT)filter method to eliminate these standing waves for every individual spectrum.The FFT filter can decrease the rms from 3.2 to 1.15 times the theoretical estimate.Compared to other methods such as sine fitting and running median,the FFT filter achieves a median rms of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%.Additionally,the FFT filter method avoids the flux loss issue encountered with some other methods.The FFT is also efficient in detecting harmonic radio frequency interference(RFI).In the FAST data,we identified three distinct types of harmonic RFI,each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1,0.5,and 0.37 MHz,respectively.The FFT filter,proven as the most effective method,is integrated into the H I data calibration and imaging pipeline for FAST(HiFAST,https://hifast.readthedocs.io).展开更多
Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the mach...Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning models.Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks.The Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being evaluated.Three popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications.Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training set.According to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set.Meanwhile,different augmentation strategies have considerably varied effects on different types of galaxies.FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,respectively.Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models.Finally,the best augmentation strategies for each galaxy category are suggested.展开更多
基金supported by the China National Key Program for Science and Technology Research and Development of China (2022YFA1602901,2023YFA1608204)the National SKA Program of China (No.2022SKA0110201)+5 种基金the National Natural Science Foundation of China (NSFC,grant Nos.11873051,11988101,12033008,12041305,12125302,12173016,and 12203065)the CAS Project for Young Scientists in Basic Research grant (No.YSBR-062)the K.C.Wong Education Foundationthe science research grants from the China Manned Space Projectsupport from the Cultivation Project for FAST Scientific Payoff and Research Achievement of CAMS-CASsupported by the China Postdoctoral Science Foundation grant No.2024M763213
文摘The standing waves existing in radio telescope data are primarily due to reflections among the instruments,which significantly impact the spectral quality of the Five-hundred-meter Aperture Spherical radio Telescope(FAST).Eliminating these standing waves for FAST is challenging given the constant changes in their phases and amplitudes.Over a ten-second period,the phases shift by 18°while the amplitudes fluctuate by 6 mK.Thus,we developed the fast Fourier transform(FFT)filter method to eliminate these standing waves for every individual spectrum.The FFT filter can decrease the rms from 3.2 to 1.15 times the theoretical estimate.Compared to other methods such as sine fitting and running median,the FFT filter achieves a median rms of approximately 1.2 times the theoretical expectation and the smallest scatter at 12%.Additionally,the FFT filter method avoids the flux loss issue encountered with some other methods.The FFT is also efficient in detecting harmonic radio frequency interference(RFI).In the FAST data,we identified three distinct types of harmonic RFI,each with amplitudes exceeding 100 mK and intrinsic frequency periods of 8.1,0.5,and 0.37 MHz,respectively.The FFT filter,proven as the most effective method,is integrated into the H I data calibration and imaging pipeline for FAST(HiFAST,https://hifast.readthedocs.io).
基金supported by China Manned Space Program through its Space Application System,the National Natural Science Foundation of China(NSFC,grant Nos.11973022 and U1811464)the Natural Science Foundation of Guangdong Province(No.2020A1515010710)。
文摘Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning models.Therefore this article proposes an image data augmentation method that combines few-shot learning and generative adversarial networks.The Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being evaluated.Three popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology classifications.Experiment results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training set.According to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data set.Meanwhile,different augmentation strategies have considerably varied effects on different types of galaxies.FSL-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,respectively.Experimental comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning models.Finally,the best augmentation strategies for each galaxy category are suggested.
