Automated Classification of Sloan Digital Sky Survey (SDSS) Stellar Spectra using Artificial Neural Networks

TL;DR

This paper presents an automated method using Probabilistic Neural Networks to classify approximately 5000 SDSS stellar spectra into 158 spectral types, facilitating large-scale astronomical data analysis.

Contribution

It introduces the application of PNN for efficient classification of SDSS stellar spectra, handling large datasets with high accuracy.

Findings

Successful classification of 5000 spectra into 158 types

Demonstrated effectiveness of PNN in astronomical spectral analysis

Enhanced automation in large-scale stellar classification

Abstract

Automated techniques have been developed to automate the process of classification of objects or their analysis. The large datasets provided by upcoming spectroscopic surveys with dedicated telescopes urges scientists to use these automated techniques for analysis of such large datasets which are now available to the community. Sloan Digital Sky Survey (SDSS) is one of such surveys releasing massive datasets. We use Probabilistic Neural Network (PNN) for automatic classification of about 5000 SDSS spectra into 158 spectral type of a reference library ranging from O type to M type stars.