Disentangling auroral, cloud and magnetic spot driven variability in three early L-dwarfs with HST/WFC3
arXiv:2603.24663 · doi:10.1051/0004-6361/202558386
Abstract
Variability monitoring provides an unparalleled insight into the atmospheric processes of brown dwarfs and directly imaged exo-planets. Inhomogeneous clouds, aurorae and magnetic spots have all been postulated as potential drivers of variability. While objects at the L/T transition have had their variability studied extensively, the variability of early L-dwarfs remains an understudied region of the parameter space. We use observations from the Hubble Space Telescope in the near-infrared, using WFC3/G141 to disentangle the drivers of variability in three known variable early L-dwarfs: 2MASS J1721039+334415, 2MASS J00361617+1821104 and 2MASS J19064801+4011089. We find that all three objects exhibit significant variability at all wavelengths, with white-light amplitudes of 0.53-1.41 %. We find that their colour variations are brighter and bluer compared to later spectral types, except for 2MASSJ19064801+4011089 which exhibits largely grey variations. We report a new period for 2MASS J1721039+334415, of 4.9^{+0.4}_{-0.2} hours. We find evidence of long term light curve stability in each object, which may indicate the presence of long lived features on their surfaces. We create a flexible modelling framework to model three potential drivers of variability: clouds, aurorae and magnetic spots. We fit our models to the spectral variability amplitude from 1.1-1.67 μm of each object. We find that changing cloud properties or magnetic spots are the most likely drivers of variability in each object. Auroral models do not reproduce the variability within the HST wavelengths, however future observations at longer wavelengths that probe higher in the atmosphere may be more sensitive to auroral effects. This work provides a foundation for future variability studies of early L-dwarfs and directly imaged exoplanets to disentangle auroral, cloud and magnetic spot driven variability.
22 pages, 18 figures
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