Deep Learning application for stellar parameters determination: I- Constraining the hyperparameters

TL;DR

This paper demonstrates how to optimize deep learning hyperparameters using CNNs to accurately determine stellar parameters from synthetic noisy spectra, highlighting the importance of tailored configurations for different parameters and data qualities.

Contribution

It provides a step-by-step pedagogical approach for hyperparameter selection in deep learning models applied to stellar spectra analysis, including synthetic data testing and adaptability to various spectral types.

Findings

Different stellar parameters require distinct hyperparameter combinations.

Maximum accuracy depends on hyperparameters, S/N ratio, and network architecture.

The method is adaptable to various spectral types and wavelength ranges.

Abstract

Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep Learning techniques in the context of stellar parameters determination. Utilizing the Convolutional Neural Network architecture, we give a step by step overview of how to select the optimal parameters for deriving the most accurate values for the stellar parameters of stars: T$_{\rm{eff}}$, $\log g$, [X/H], and $v_e \sin i$. Synthetic spectra with random noise were used to constrain this method and to mimic the observations. We found that each stellar parameter requires a different combination of network hyperparameters and the maximum accuracy reached depends on this combination, as well as, the Signal to Noise ratio of the observations, and the architecture of the network. We also show that this technique can be applied to other spectral types in different wavelength ranges after the technique has been optimized.