ASERA: A Spectrum Eye Recognition Assistant for Quasar Spectra

TL;DR

ASERA is a semi-automated, interactive software toolkit designed to improve the efficiency and accuracy of spectral classification for quasars, stars, and galaxies, especially in large sky surveys with low-quality spectra.

Contribution

It introduces a user-friendly, semi-automated spectral recognition tool that integrates visualization, template superimposition, and interactive features for improved classification accuracy.

Findings

Effective in recognizing quasar spectra from LAMOST survey

Redshift estimation and spectral line identification functionalities

Reduces manual inspection effort for low-quality spectra

Abstract

Spectral type recognition is an important and fundamental step of large sky survey projects in the data reduction for further scientific research, like parameter measurement and statistic work. It tends out to be a huge job to manually inspect the low quality spectra produced from massive spectroscopic survey, where the automatic pipeline may not provide confident type classification results. In order to improve the efficiency and effectiveness of spectral classification, we develop a semi-automated toolkit named ASERA, A Spectrum Eye Recognition Assistant. The main purpose of ASERA is to help the user in quasar spectral recognition and redshift measurement. Furthermore it can also be used to recognize various types of spectra of stars, galaxies and AGNs (Active Galactic Nucleus). It is an interactive software allowing the user to visualize observed spectra, superimpose template spectra from the Sloan Digital Sky Survey (SDSS), and interactively access related spectral line information. It is an efficient and user-friendly toolkit for accurate classification of spectra observed by LAMOST (the Large Sky Area Multi-object Fiber Spectroscopic Telescope). The toolkit is available in two modes: a Java standalone application and a Java applet. ASERA has a few functions, such as wavelength and flux scale setting, zoom in and out, redshift estimation, spectral line identification, which helps user to improve the spectral classification accuracy especially for low quality spectra and reduce the labor of eyeball check. The function and performance of this tool is displayed through the recognition of several quasar spectra and a late type stellar spectrum from the LAMOST Pilot survey. Its future expansion capabilities are discussed.