Double-peaked Emission-line Galaxies Research Articles

Research on the Double-peaked Emission-line Galaxies from LAMOST Survey

Research on the Double-peaked Emission-line Galaxies from LAMOST Survey

Stellar population analysis on the stacked spectra of double-peaked emission-line galaxies

Double-peaked emission-line galaxies have long been perceived as objects related to merging galaxies or other phenomenawith disturbed dynamical activities, such as outflows and disk rotation. In order to find the connection between the unique activities happening in these objects and their stellar population physics, we study the stellar populations of the stacked spectra drawn from double-peaked emission-line galaxies in the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 4 (DR4) and the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) databases. We group the selected double-peaked emission-line objects into 10 different types of pairs based on the Baldwin-Phillips-Terlevich (BPT) diagnosis for each pair of blueshifted and redshifted components, and then stack the spectra of each group for analysis. The software STARLIGHT is employed to fit each stacked spectrum, and the contributions of stars at different ages and metallicities are quantified for subsequent comparative study and analysis. To highlight the commonality and uniqueness in these double-peaked emitting objects, we compare the population synthesis results of the stacked spectra of double-peaked emission-line galaxies with those of their counterpart reference samples displaying single-peaked emission features. The reference samples are also selected from the LAMOST DR4 and SDSS DR7 databases. From the comparison results, we confirm the strong correlations between stellar populations and their spectral classes, and find that the double-peaked emitting phenomenon is more likely to occur in an ‘older’ stellar environment and the subgroups hosting different BPT components will show an obvious heterogeneous star formation history.

The weirdest SDSS galaxies: results from an outlier detection algorithm

How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and close galaxy pairs. We find galaxies with high ionisation lines, galaxies which host supernovae, and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them peculiar. It can be executed on imaging, time-series, and other spectroscopic data, operates well with thousands of features, is not sensitive to missing values, and is easily parallelisable.