A reliable activity proxy in SPIRou spectra of M dwarfs using machine learning

¹ Université de Toulouse, CNRS, IRAP, 14 av. Edouard Belin, 31400 Toulouse, France
² Center for Astrophysics | Harvard & Smithsonian, 60 Garden street, Cambridge, MA 02138, USA
³ Univ. Grenoble Alpes, CNRS, IPAG, 38000 Grenoble, France
⁴ Université de Lyon, INSA-Lyon, CNRS, LIRIS, 69621 Lyon, France
⁵ Université Côte d'Azur, Observatoire de la Côte d'Azur, CNRS, Laboratoire Lagrange 06903, France
⁶ Institut Trottier de recherche sur les exoplanètes, Département de Physique, Université de Montréal, Montréal, Québec, Canada
⁷ Observatoire du Mont-Mégantic, Québec, Canada
⁸ Aix Marseille Univ, CNRS, CNES, LAM, Marseille, France
⁹ Laboratoire Univers et Particules de Montpellier, Université de Montpellier, CNRS, 34095 Montpellier, France
¹⁰ Leiden Observatory, Leiden University, Niels Bohrweg 2, 2333 CA Leiden, The Netherlands

^★ Corresponding author: Paul.Charpentier@irap.omp.eu

Received: 28 March 2025
Accepted: 16 June 2025

Abstract

Context. Recent instruments have extended radial velocity observations from the optical domain to the near-infrared range (NIR). In particular, this has allowed M dwarfs to be studied more extensively, which is notable because they are known to host rocky planets more frequently. However, these stars also have, on average, stronger magnetic activity compared to solar-type stars, and investigating this magnetic activity is key to uncovering any planets around such stars.

Aims. This paper aims to extensively test a new reliable magnetic activity indicator named W₁ and confirm it as a proxy for the small-scale magnetic field for M dwarf stars.

Methods. The magnetic activity indicator W₁ is derived from a principal component analysis (PCA) applied on the per-line differential line width (dLW). However, the PCA is highly sensitive to contamination from telluric residuals in the spectra. Therefore, we employed a filtering technique based on such machine learning tools as the unsupervised dimensional reduction (DR) algorithm and support vector machine (SVM) to remove faulty lines. We assessed the performance of this filtering method using a simulation of observations of the per-line dLW variations before applying it to NIR high-resolution spectroscopic observations from SPIRou (at the Canada-France-Hawaii Telescope) of five targets with various stellar magnetic activity levels, spectral types, and rotation periods contained in the SPIRou Legacy Survey, namely AU Mic, EV Lac, GJ1286, GJ1289, and G1 410.

Results. The filtered W₁ signal is modulated with a period consistent with the rotation period retrieved from activity indicators, corresponding to the magnetic activity for all the stars studied. It also correlates with the small-scale magnetic field of all five stars, with a direct Pearson correlation coefficient greater than 0.80. Additionally, we identified 201 stellar lines that are particularly sensitive to magnetic activity that could be valuable for the study of magnetic fields.