Identification of Artifacts and Interesting Celestial Objects in LAMOST Spectral Survey

TL;DR

This paper presents a method for identifying artifacts and rare celestial objects in LAMOST spectral data using clustering and outlier analysis on a Hadoop cluster, uncovering potential quasars, blazars, and Be-stars.

Contribution

It introduces a scalable approach combining clustering and outlier detection on large spectral datasets to find interesting and rare celestial objects and artifacts.

Findings

Identified several hundred artifact-dominated spectra

Discovered tens of interesting emission-line spectra

Potential new quasars, blazars, and Be-stars

Abstract

The LAMOST DR1 survey contains about two million of spectra labelled by its pipeline as stellar objects of common spectral classes. There is, however, a lot of spectra corrupted in some way by both instrumental and processing artifacts, which may mimic spectral properties of interesting celestial objects, namely emission lines of Be stars and quasars. We have tested several clustering methods as well as outliers analysis on a sample of one hundred thousand spectra using Spark scripts running on Hadoop cluster consisting of twenty-four sixteen-core nodes. This experiment was motivated by an attempt to find rare objects with interesting spectra as outliers most dissimilar from all common spectra. The result of this time-consuming procedure is a list of several hundred candidates where different artifacts are prominent, but also tens of very interesting emission-line spectra requiring further detailed examination. Many of them may be quasars or even blazars as well as yet unknown Be-stars. It deserves mentioning that most of the work benefitted considerably from technologies of Virtual Observatory.