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We present a novel machine-learning approach for detecting faint point sources in high-contrast adaptive optics (AO) imaging data sets. The most widely used algorithms for primary subtraction aim to decouple bright stellar speckle noise from planetary signatures by subtracting an approximation of the temporally evolving stellar noise from each frame in an imaging sequence. Our approach aims to improve the stellar noise approximation and increase the planet detection sensitivity by leveraging deep learning in a novel direct imaging post-processing algorithm. We show that a convolutional autoencoder neural network, trained on an extensive reference library of real imaging sequences, accurately reconstructs the stellar speckle noise at the location of a potential planet signal. This tool is used in a post-processing algorithm we call Direct Exoplanet Detection with Convolutional Image Reconstruction, orConStruct. The reliability and sensitivity ofConStructare assessed using real Keck/NIRC2 angular differential imaging data sets. Of the 30 unique point sources we examine,ConStructyields a higher signal-to-noise ratio than traditional principal component analysis-based processing for 67% of the cases and improves the relative contrast by up to a factor of 2.6. This work demonstrates the value and potential of deep learning to take advantage of a diverse reference library of point-spread function realizations to improve direct imaging post-processing.ConStructand its future improvements may be particularly useful as tools for post-processing high-contrast images from JWST and extreme AO instruments, both for the current generation and those being designed for the upcoming 30 m class telescopes.

Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and brown dwarfs. Here, we report the independent discovery of HIP 21152 B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atmospheric characterization of the system. HIP 21152 was targeted in an ongoing high-contrast imaging campaign of stars exhibiting proper-motion changes between Hipparcos and Gaia, and was also recently identified by Bonavita et al. (2022) and Kuzuhara et al. (2022). Our Keck/NIRC2 and SCExAO/CHARIS imaging of HIP 21152 revealed a comoving companion at a separation of 0.″37 (16 au). We perform a joint orbit fit of all available relative astrometry and radial velocities together with the Hipparcos-Gaia proper motions, yielding a dynamical mass of${24}_{-4}^{+6}{M}_{\mathrm{Jup}}$, which is 1–2σlower than evolutionary model predictions. Hybrid grids that include the evolution of cloud properties best reproduce the dynamical mass. We also identify a comoving wide-separation (1837″ or 7.9 × 10⁴au) early-L dwarf with an inferred mass near the hydrogen-burning limit. Finally, we analyze the spectra and photometry of HIP 21152 B using the Saumon & Marley (2008) atmospheric models and a suite of retrievals. The best-fit grid-based models havef_sed= 2, indicating the presence of clouds,T_eff= 1400 K, and$\log g=4.5\mathrm{dex}$. These results are consistent with the object’s spectral type of T0 ± 1. As the first benchmark brown dwarf companion in the Hyades, HIP 21152 B joins the small but growing number of substellar companions with well-determined ages and dynamical masses.