New substellar candidates identified through deep learning in the F150 sample of the large-scale SHINE direct imaging survey

Context. The SPHERE High-contrast Imaging survey for Exoplanets represents one of the largest direct imaging campaigns, targeting over 400 young, nearby stars with the goal of detecting and characterizing giant exoplanets and brown dwarfs. The new analysis still awaits peer review, but it already lays out the central claim clearly.

That matters because exoplanet science has moved beyond the era of simple discovery into a period of comparative characterization. With more than five thousand confirmed planets known, the scientifically productive questions now concern atmospheric composition, internal structure, orbital history and the statistical properties of populations rather than the existence of individual worlds. A new detection or spectral measurement is most valuable when it adds a well-constrained data point to those comparative frameworks, not when it stands alone as an anecdote. The SPHERE High-contrast Imaging survey for Exoplanets (SHINE) represents one of the largest direct imaging campaigns, targeting over 400 young, nearby stars with the goal of. This dataset offers a unique opportunity to revisit observations using modern, data-driven approaches, potentially uncovering new substellar candidates that may have been.

Our study focuses on reprocessing and reanalyzing the so-called F150 sample, a well-defined subset of 150 main-sequence stars within 100 pc observed in the H-band with VLT/SPHERE. We apply NA-SODINN, a supervised deep learning model specifically tailored for detecting faint planetary signals in angular differential imaging (ADI) sequences.

Designed to model local noise properties and capture spatial context, NA-SODINN is effective at distinguishing real companions from residual speckle noise. To translate the model's pixel-wise confidence maps into actionable detections, we introduce a novel F1-score-based thresholding strategy.

This principled approach balances sensitivity and specificity, addressing a key limitation in current deep learning-based methods. NA-SODINN recovers all known companions and some of the debris disks in the F150 sample, and identifies 13 new substellar candidates not reported in previous studies: ten detected.

The broader interest lies in making the target less anecdotal and more comparable with the rest of the known planetary population. Population-level questions, such as the frequency of atmospheres around small rocky planets or the prevalence of water-rich worlds in the habitable zone, require well-characterized individual data points before statistical patterns become meaningful. Each new planet with a measured radius, mass and, ideally, atmospheric constraint is a brick in that larger structure, and the accumulation of bricks eventually allows theorists to test formation models against real distributions rather than projections.

For the ten sources detected in both bands, we use the H2-H3 color-magnitude diagram to perform a first assessment of their nature. Based on this analysis, we identify two ambiguous cases and three photometrically promising candidates.

Because this is still a preprint, the result should be read with genuine interest and proportionate caution. Peer review is not a guarantee of correctness, but it is a process that forces authors to respond to technical criticism from specialists who have no stake in a particular outcome. Preprints that survive that process, often with substantive revisions, emerge with a stronger evidential base than the version that first appeared. Until that stage is complete, the responsible reading keeps uncertainty explicitly visible rather than treating the claims as established findings.

The next step is to improve independent constraints on the mass, radius, atmospheric composition and orbital dynamics of the target. Transmission spectroscopy with JWST, radial velocity campaigns with high-resolution ground-based spectrographs and phase-curve measurements from space photometry represent the observational toolkit that can move characterization from plausible to robust. That convergence of techniques is the standard the community now expects before a planetary atmosphere result is treated as confirmed. Until peer review and independent follow-up address those open questions, skepticism is not a failure of appreciation for the work; it is part of how science decides what to keep.

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