arXiv:2506.16255v2 Announce Type: replace-cross
Abstract: Conventional galaxy generation methods rely on semi-analytical models and hydrodynamic simulations, which are highly dependent on physical assumptions and parameter tuning. In contrast, data-dr…
arXiv:2604.02156v1 Announce Type: new
Abstract: Scientific multi-label text classification suffers from extreme class imbalance, where specialized terminology exhibits severe power-law distributions that challenge standard classification approaches. E…
arXiv:2604.00993v1 Announce Type: cross
Abstract: The direct imaging of potentially habitable exoplanets is one prime science case for high-contrast imaging instruments on extremely large telescopes. Most such exoplanets orbit close to their host star…
arXiv:2603.29660v1 Announce Type: cross
Abstract: Next-generation radio astronomy surveys are producing millions of resolved sources, but robust morphology analysis remains difficult across heterogeneous telescopes and imaging pipelines. We present ST…
arXiv:2603.27830v1 Announce Type: cross
Abstract: As the population of anthropogenic space objects transitions from sparse clusters to mega-constellations exceeding 100,000 satellites, traditional orbital propagation techniques face a critical bottlen…
arXiv:2603.26777v1 Announce Type: cross
Abstract: The Event Horizon Telescope (EHT) delivered the first image of a black hole by capturing the light from its surrounding accretion flow, revealing structure but not dynamics. Simulations of black hole a…
arXiv:2603.26953v1 Announce Type: cross
Abstract: The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing atmospheric compositio…
arXiv:2603.23899v2 Announce Type: replace-cross
Abstract: We present SM-Net, a machine-learning model that learns a continuous spectral manifold from multiple high-resolution stellar libraries. SM-Net generates stellar spectra directly from the fundam…
arXiv:2603.26489v1 Announce Type: cross
Abstract: For frontier experiments operating at the edge of detectability, instrument design directly determines the probability of discovery. We introduce Conditional Neural Bayes Ratio Estimation (cNBRE), whic…
arXiv:2603.26644v1 Announce Type: new
Abstract: We present Automatic Laplace Collapsed Sampling (ALCS), a general framework for marginalising latent parameters in Bayesian models using automatic differentiation, which we combine with nested sampling t…