# Sliced Inverse Regression: application to fundamental stellar parameters

## TL;DR

This paper introduces a regularized sliced inverse regression (RSIR) method for accurately deriving fundamental stellar parameters from spectra, demonstrating its effectiveness on synthetic and observed stellar data with improved accuracy and efficiency.

## Contribution

The paper presents a novel RSIR approach combined with PCA for stellar parameter estimation, outperforming traditional methods in accuracy and computational efficiency.

## Key findings

- Achieved internal errors smaller than parameter sampling steps.
- Successfully applied to synthetic and observed stellar spectra.
- Provided consistent results with previous studies for well-known stars.

## Abstract

We present a method for deriving stellar fundamental parameters. It is based on a regularized sliced inverse regression (RSIR). We first tested it on noisy synthetic spectra of A, F, G, and K-type stars, and inverted simultaneously their atmospheric fundamental parameters: Teff, log g, [M/H] and vsini. Different learning databases were calculated using a range of sampling in Teff, log g, vsini, and [M/H]. Combined with a principal component analysis (PCA) nearest neighbors (NN) search, the size of the learning database is reduced. A Tikhonov regularization is applied, given the ill-conditioning of SIR. For all spectral types, decreasing the size of the learning database allowed us to reach internal accuracies better than PCA-based NN-search using larger learning databases. For each analyzed parameter, we have reached internal errors that are smaller than the sampling step of the parameter. We have also applied the technique to a sample of observed FGK and A stars. For a selection of well studied stars, the inverted parameters are in agreement with the ones derived in previous studies. The RSIR inversion technique, complemented with PCA pre-processing proves to be efficient in estimating stellar parameters of A, F, G, and K stars.