Estimating stellar fundamental parameters using PCA: application to early type stars of GES data

TL;DR

This paper presents a PCA-based method to estimate key stellar parameters from synthetic spectra, demonstrating promising results when applied to Gaia ESO Survey data.

Contribution

It introduces a novel PCA-based approach for deriving stellar parameters from spectra, improving efficiency and accuracy over traditional methods.

Findings

Effective parameter estimation from synthetic spectra

Successful application to Gaia ESO Survey data

Potential for broad application in stellar spectroscopy

Abstract

This work addresses a procedure to estimate fundamental stellar parameters such as T eff , logg, [Fe/H], and v sin i using a dimensionality reduction technique called Principal Component Analysis (PCA), applied to a large database of synthetic spectra. This technique shows promising results for inverting stellar parameters of observed targets from Gaia ESO Survey.