ASPECT: A spectra clustering tool for exploration of large spectral surveys

TL;DR

ASPECT is a semi-automated spectral clustering tool utilizing a neural network-based self-organizing map, enabling visual exploration and analysis of large spectral datasets like SDSS with over 600,000 spectra.

Contribution

It introduces a novel, user-friendly spectral clustering method combining neural networks and visual analysis for large spectral archives.

Findings

Successfully clustered 0.6 million SDSS spectra

Connected spectral data with external catalogs and morphological info

Demonstrated effective exploration of large spectral datasets

Abstract

We present the novel, semi-automated clustering tool ASPECT for analysing voluminous archives of spectra. The heart of the program is a neural network in form of Kohonen's self-organizing map. The resulting map is designed as an icon map suitable for the inspection by eye. The visual analysis is supported by the option to blend in individual object properties such as redshift, apparent magnitude, or signal-to-noise ratio. In addition, the package provides several tools for the selection of special spectral types, e.g. local difference maps which reflect the deviations of all spectra from one given input spectrum (real or artificial). ASPECT is able to produce a two-dimensional topological map of a huge number of spectra. The software package enables the user to browse and navigate through a huge data pool and helps him to gain an insight into underlying relationships between the spectra and other physical properties and to get the big picture of the entire data set. We demonstrate the capability of ASPECT by clustering the entire data pool of 0.6 million spectra from the Data Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results regarding quality and completeness we track objects from existing catalogues of quasars and carbon stars, respectively, and connect the SDSS spectra with morphological information from the GalaxyZoo project.