A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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The Milky Way stellar disk shows a broken radial density profile with four components, azimuthal dependence, inner and outer flaring, and a density-metallicity bump possibly from radial migration.
TOI-4311 hosts a 0.99-day super-Earth (1.38 R_earth, 4.5 M_earth) and 15-day sub-Neptune (2.47 R_earth), plus a candidate 38-day planet, with the dense inner planet potentially challenging formation theories given the host's galactic population.
Collates archival stellar activity and rotation data for potential HWO targets, finding measurements for at least 70% of high-interest systems but activity cycles for fewer than 20%.
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.
citing papers explorer
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Trajectory-Agnostic Asteroid Detection in TESS with Deep Learning
A W-Net deep learning model detects asteroids in TESS data independently of trajectory by rotating training image cubes and using adaptive normalization for data scaling.
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Mapping the Milky Way with Gaia Bp/Rp spectra-IV: the broken and asymmetric density profile of the stellar disk traced by a large sample of red clumps
The Milky Way stellar disk shows a broken radial density profile with four components, azimuthal dependence, inner and outer flaring, and a density-metallicity bump possibly from radial migration.
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An Ultra-Short Period Super-Earth and a Sub-Neptune Orbiting the K dwarf TOI-4311
TOI-4311 hosts a 0.99-day super-Earth (1.38 R_earth, 4.5 M_earth) and 15-day sub-Neptune (2.47 R_earth), plus a candidate 38-day planet, with the dense inner planet potentially challenging formation theories given the host's galactic population.
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HWO Target Stars and Systems: Activity and Rotation Catalog (ARC) of Potential Target Stars for the Habitable Worlds Observatory
Collates archival stellar activity and rotation data for potential HWO targets, finding measurements for at least 70% of high-interest systems but activity cycles for fewer than 20%.
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Machine Learning as a Transformative Tool for (Exo-)Planetary Science
The paper reviews ML applications for sequence modeling, pattern recognition, and generative Bayesian analysis to tackle heterogeneous data challenges in (exo)planetary science.