The Stellar parametrization using Artificial Neural Network

TL;DR

This paper discusses an automated method for stellar parameter estimation using artificial neural networks trained on medium resolution spectra, enabling rapid analysis of field stars.

Contribution

It introduces a neural network-based approach for estimating stellar parameters from medium resolution spectra, improving speed and automation in stellar analysis.

Findings

Preliminary results demonstrate effective parameter estimation.

Neural networks trained on calibrating stars can predict parameters of unknown stars.

Method shows promise for large-scale stellar surveys.

Abstract

An update on recent methods for automated stellar parametrization is given. We present preliminary results of the ongoing program for rapid parametrization of field stars using medium resolution spectra obtained using Vainu Bappu Telescope at VBO, Kavalur, India. We have used Artificial Neural Network for estimating temperature, gravity, metallicity and absolute magnitude of the field stars. The network for each parameter is trained independently using a large number of calibrating stars. The trained network is used for estimating atmospheric parameters of unexplored field stars.