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Spectral type recognition is an important and fundamental step of large skysurvey projects in the data reduction for further scientific research, likeparameter measurement and statistic work. It tends out to be a huge job tomanually inspect the low quality spectra produced from massive spectroscopicsurvey, where the automatic pipeline may not provide confident typeclassification results. In order to improve the efficiency and effectiveness ofspectral classification, we develop a semi-automated toolkit named ASERA, ASpectrum Eye Recognition Assistant. The main purpose of ASERA is to help theuser in quasar spectral recognition and redshift measurement. Furthermore itcan also be used to recognize various types of spectra of stars, galaxies andAGNs (Active Galactic Nucleus). It is an interactive software allowing the userto visualize observed spectra, superimpose template spectra from the SloanDigital Sky Survey (SDSS), and interactively access related spectral lineinformation. It is an efficient and user-friendly toolkit for accurateclassification of spectra observed by LAMOST (the Large Sky Area Multi-objectFiber Spectroscopic Telescope). The toolkit is available in two modes: a Javastandalone application and a Java applet. ASERA has a few functions, such aswavelength and flux scale setting, zoom in and out, redshift estimation,spectral line identification, which helps user to improve the spectralclassification accuracy especially for low quality spectra and reduce the laborof eyeball check. The function and performance of this tool is displayedthrough the recognition of several quasar spectra and a late type stellarspectrum from the LAMOST Pilot survey. Its future expansion capabilities arediscussed.

ASERA: A Spectrum Eye Recognition Assistant for Quasar Spectra