Estimating accurate reddening values of LAMOST M dwarfs

TL;DR

This paper presents a machine learning approach to accurately estimate reddening values of over 640,000 M dwarfs using LAMOST spectra and Gaia photometry, enabling detailed mapping of local interstellar dust.

Contribution

It introduces a novel method combining LAMOST spectra, Gaia data, and Random Forest regression to derive high-precision reddening values for M dwarfs.

Findings

Reddening uncertainty as low as 0.03 mag.

Reddening coefficient as a function of stellar colour and reddening.

Catalogue of E(B-V) values for over 640,000 M dwarfs.

Abstract

M dwarfs are the dominating type of stars in the solar neighbourhood. They serve as excellent tracers for the study of the distribution and properties of the nearby interstellar dust. In this work, we aim to obtain high accuracy reddening values of M dwarf stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 8 (DR8). Combining the LAMOST spectra with the high-quality optical photometry from the Gaia Early Data Release 3 (Gaia EDR3), we have estimated the reddening values $E(G_{\rm BP}-G_{\rm RP})$ of 641,426 M dwarfs with the machine-learning algorithm Random Forest regression. The typical reddening uncertainty is only 0.03 mag in $E(G_{\rm BP}-G_{\rm RP})$. We have obtained the reddening coefficient $R_{(G_{\rm BP}-G_{\rm RP})}$, which is a function of the stellar intrinsic colour $(G_{\rm BP}-G_{\rm RP})_0$ and reddening value $E(B-V)$. The values of $E(B-V)$ are also provided for the individual stars in our catalogue. Our resultant high accuracy reddening values of M dwarfs, combined with the Gaia parallaxes, will be very powerful to map the fine structures of the dust in the solar neighbourhood.