Magnetic activity of ultracool dwarfs in the LAMOST DR11

TL;DR

The paper investigates magnetic activity in ultracool dwarfs by expanding the UCD census in LAMOST DR11 and evaluating CSST slitless spectroscopy through degraded-spectrum simulations. It employs cross-matching and a semi-supervised autoencoder to identify late-M dwarfs, identifying 962 new UCDs and validating the CSST approach. Magnetic activity is quantified via Hα emission, with over 80% of late-M dwarfs active and activity correlating with spectral type; rotation periods derived from Kepler/K2 data yield a saturated activity–rotation relation with Ro_sat ≈ 0.12. The work highlights the CSST era’s potential for large-scale UCD studies and discusses the benefits of multiwavelength follow-up (including UV and IR) for improved age and activity diagnostics.

Abstract

Ultracool dwarfs consist of lowest-mass stars and brown dwarfs. Their interior is fully convective, different from that of the partly-convective Sun-like stars. Magnetic field generation process beneath the surface of ultracool dwarfs is still poorly understood and controversial. To increase samples of active ultracool dwarfs significantly, we have identified 962 ultracool dwarfs in the latest LAMOST data release, DR11. We also simulate the Chinese Space Station Survey Telescope (CSST) low-resolution slitless spectra by degrading the LAMOST spectra. A semi-supervised machine learning approach with an autoencoder model is built to identify ultracool dwarfs with the simulated CSST spectra, which demonstrates the capability of the CSST all-sky slitless spectroscopic survey on the detection of ultracool dwarfs. Magnetic activity of the ultracool dwarfs is investigated by using the H$α$ line emission as a proxy. The rotational periods of 82 ultracool dwarfs are derived based on the Kepler/K2 light curves. We also derive the activity-rotation relation of the ultracool dwarfs, which is saturated around a Rossby number of 0.12.

Figures (10)

• Figure 1: The left panel shows examples of the simulated CSST spectra of M7 dwarf and giant stars, as well as the reconstructed results from the autoencoder. The right panel shows the saliency map for the neural networks on a dwarf spectrum.
• Figure 2: Structure of the semi-supervised classification model with an autoencoder.
• Figure 3: Confusion diagram for the classification on the test data set.
• Figure 4: Left panel is color-magnitude diagram for the whole M stars (blue), the LAMOST UCDs identified by wang2022 (orange) and this work (green). Here, we do not take into account the interstellar extinction and reddening. Right panel shows distributions of spectral types for the UCDs identified by wang2022 (orange) and this work (green).
• Figure 5: Toomre diagram of $UVW$ velocities. The dashed lines show the total velocities of 70 and 180 km/s.