利用TAUVEX/ASTROSAT卫星数据管道的光谱和综合波段数据的自动星-星系分离
Automated star-galaxy segregation using spectral and integrated band data for TAUVEX/ASTROSAT satellite data pipeline
摘要：
我们采用人工神经网络（ANN）为基础的技术开发一个管道，自动分离的恒星从星系观察特拉维夫大学紫外线实验（TAUVEX）。我们使用UVBLUE数据库中的恒星合成光谱和国际紫外探测器（IUE）1250 ~ 3220 nm波段的星系低分辨率光谱作为训练集，恒星和星系的IUE低分辨率光谱作为测试集。所有的数据集已被预处理，以获得带积分通量，以模仿TAUVEX紫外成像仪的观测。我们还使用全长光谱特征（这也将是有用的ASTROSAT任务）进行基于ANN的分离方案。我们的研究结果表明，在不提供全光谱特征的情况下，有限的波段综合特征可以用来分离两类对象;虽然波段数据分类不如全光谱数据分类准确。
英文摘要：
We employ an Artificial Neural Network (ANN) based technique to develop a pipeline for automated segregation of stars from the galaxies to be observed by Tel-Aviv University Ultra-Violet Experiment (TAUVEX). We use synthetic spectra of stars from UVBLUE library and selected International Ultraviolet Explorer (IUE) low resolution spectra for galaxies in the ultraviolet (UV) region from 1250 to 3220Åas the training set and IUE low-resolution spectra for both the stars and the galaxies as the test set. All the data sets have been pre-processed to get band integrated fluxes so as to mimic the observations of the TAUVEX UV imager. We also perform the ANN based segregation scheme using the full length spectral features (which will also be useful for the ASTROSAT mission). Our results suggest that, in the case of the non-availability of full spectral features, the limited band integrated features can be used to segregate the two classes of objects; although the band data classification is less accurate than the full spectral data classification.
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