On the use of machine learning algorithms in the measurement of stellar magnetic fields

Abstract

Regression methods based in Machine Learning Algorithms (MLA) have become an important tool for data analysis in many different disciplines. In this work, we use MLA in an astrophysical context; our goal is to measure the mean longitudinal magnetic field in stars (H_ eff) from polarized spectra of high resolution, through the inversion of the so-called multi-line profiles. Using synthetic data, we tested the performance of our technique considering different noise levels: In an ideal scenario of noise-free multi-line profiles, the inversion results are excellent; however, the accuracy of the inversions diminish considerably when noise is taken into account. In consequence, we propose a data pre-process in order to reduce the noise impact, which consists in a denoising profile process combined with an iterative inversion methodology. Applying this data pre-process, we have found a considerable improvement of the inversions results, allowing to estimate the errors associated to the measurements of stellar magnetic fields at different noise levels. We have successfully applied our data analysis technique to two different stars, attaining by first time the measurement of H_eff from multi-line profiles beyond the condition of line autosimilarity assumed by other techniques.

Accepted for publication in A&A

References in corpus (10)

• Toroidal versus poloidal magnetic fields in Sun-like stars: a rotation threshold
• The MiMeS Survey of Magnetism in Massive Stars: Introduction and overview
• The MiMeS survey of magnetism in massive stars: Magnetic analysis of the O-type stars
• Oscillation frequencies for 35 \Kepler solar-type planet-hosting stars using Bayesian techniques and machine learning
• K2 Variable Catalogue II: Machine Learning Classification of Variable Stars and Eclipsing Binaries in K2 Fields 0-4
• Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning
• Sparse inversion of Stokes profiles. I. Two-dimensional Milne-Eddington inversions
• On The Statistical Properties of the Lower Main Sequence
• Asteroseismic determination of fundamental parameters of sun-like stars using multi-layered neural networks
• Stellar longitudinal magnetic field determination through multi-Zeeman signatures

Cited by in corpus (1)

• On the use of the first-order moment approach for measurements of H_eff from LSD profiles