研究目的
To develop a convolutional neural network (CNN) that can automatically classify a Faraday spectrum as either simple or complex, specifically for the Polarisation Sky Survey of the Universe’s Magnetism (POSSUM) early science observations.
研究成果
The convolutional neural network developed can distinguish between simple Faraday sources and those that contain two Faraday thin components with a high degree of accuracy. It demonstrates the potential for automated classification in large-scale polarization surveys like POSSUM, with ongoing work to expand its capabilities to more complex sources.
研究不足
The current work focuses only on two-component models, with both components being Faraday thin. The network's performance is limited to the parameter space defined by the training data. Future work includes extending the training to include Faraday thick and three-component sources.
1：Experimental Design and Method Selection:
The study employs a convolutional neural network (CNN) to classify Faraday spectra. The network architecture includes inception layers with parallel channels of convolutions using different kernel widths.
2：Sample Selection and Data Sources:
Simulated data representing both simple and complex Faraday spectra were generated, with parameters sampled from a uniform distribution.
3：List of Experimental Equipment and Materials:
The study is computational, focusing on the development and testing of a CNN. No physical equipment was used.
4：Experimental Procedures and Operational Workflow:
The network was trained using batch stochastic gradient-descent on a training set of 100,000 sources, with 30,000 sources withheld for cross-validation.
5：Data Analysis Methods:
The performance of the CNN was evaluated based on its ability to correctly classify Faraday spectra as simple or complex, with specific attention to false positive and false negative rates.
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