An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering

Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) has completed the observation of nearly 20 million celestial objects,including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio,these spectra often show some anomalous features that do not work well with current templates.In this paper,a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected,and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame(Spectra Analysis-Frame) is provided to explore their origins from different perspectives.The SA-Frame is composed of three parts:NAPC-Spec clustering,characterization and origin analysis.First,NAPC-Spec(Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality,resulting in 13 types.Second,characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum,where these 13 types are characterized as regular spectra with low S/Ns,splicing problems,suspected galactic emission signals,contamination from city light and un-gregarious type respectively.Third,a preliminary analysis of their origins is made from the characteristics of the observational targets,contamination from the sky,and the working status of the instruments.These results would be valuable for improving the overall data quality of large-scale spectral surveys.

Stellar spectra association rule mining method based on the weighted frequent pattern tree

Effective extraction of data association rules can provide a reliable basis for classification of stellar spectra. The concept of stellar spectrum weighted itemsets and stellar spectrum weighted association rules are introduced, and the weight of a single property in the stellar spectrum is determined by information entropy. On that basis, a method is presented to mine the association rules of a stellar spectrum based on the weighted frequent pattern tree. Important properties of the spectral line are highlighted using this method. At the same time, the waveform of the whole spectrum is taken into account. The experimental results show that the data association rules of a stellar spectrum mined with this method are consistent with the main features of stellar spectral types.

A sample of E+A galaxy candidates in the Second Data Release of LAMOST Survey

A sample of 70 E＋A galaxies is selected from 37 206 galaxies in the second data release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST）. This sample is selected according to the criteria for E＋A galaxies defined by Goto, and each of these objects is further visually identified. In this sample, most objects are low redshift E＋A galaxies with z 〈 0.25, and are located in an area of the sky with high Galactic latitude and magnitude from 14 to 18 mag in the g, r and i bands. A stellar population analysis of the whole sample indicates that the E＋A galaxies are characterized by both young and old stellar populations（SPs）, and the metalrich SPs have relatively higher contributions than the metal-poor ones. Additionally, a morphological classification of these objects is performed based on images taken from the Sloan Digital Sky Survey.

Importance of excluded duplicates reporting in a systematic review

We read with great interest the review article by Ghodsi and colleagues(Volume 17,issue 3)[1].The authors aimed to evaluate the chest computed tomography(CT)findings in infants with confirmed coronavirus disease 2019(COVID-19)infection.Certainly,the tindings of Ghodsi et al.hold significance for physicians when determining the typical characteristics of COVID-19.While agreeing with the conclusions made by the authors,we would like to draw their attention to a certain aspect.