Improving prediction performance of stellar parameters using functional models

TL;DR

This paper introduces a functional data analysis approach for predicting stellar parameters from electromagnetic spectra, combining basis decomposition, regression, and bootstrap methods to improve prediction accuracy and quantify uncertainty.

Contribution

It presents a novel two-step functional modeling framework with bootstrap-based prediction intervals for stellar parameter estimation.

Findings

Effective prediction of stellar parameters demonstrated

Bootstrap intervals provide reliable uncertainty quantification

Method outperforms traditional approaches

Abstract

This paper investigates the problem of prediction of stellar parameters, based on the star's electromagnetic spectrum. The knowledge of these parameters permits to infer on the evolutionary state of the star. From a statistical point of view, the spectra of different stars can be represented as functional data. Therefore, a two-step procedure decomposing the spectra in a functional basis combined with a regression method of prediction is proposed. We also use a bootstrap methodology to build prediction intervals for the stellar parameters. A practical application is also provided to illustrate the numerical performance of our approach.