arXiv:2606. 13868v1 Announce Type: cross Abstract: We present an end-to-end pipeline for estimating stellar parameters from Sloan Digital Sky Survey Data Release 12 spectra using a fully connected multitask neural network with residual blocks, whose hyperparameters are tuned via Bayesian optimization.
arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.
arXiv:2606. 18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity.
BEAST is the first Bayesian Swin Transformer for atmospheric forecasting at 0.25° global resolution, capable of quantifying both aleatoric and epistemic uncertainty. The authors introduce a 4D‑parallelization scheme with domain‑tensor‑parallelism and a novel uncertainty parallel method, allowing a 2.4‑billion‑parameter model to reach 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs. A 700‑million‑parameter BEAST trained on 40 years of data achieves predictive skill comparable to state‑of‑the‑art probabilistic AI models, predicts extreme events with high accuracy, and generates large ensembles 3–4× faster than the current best AI model.
arXiv:2606. 18338v1 Announce Type: new Abstract: The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets.
The paper presents a probabilistic machine‑learning framework for classifying low‑ and high‑resolution stellar and extragalactic spectra, targeting the upcoming 4MOST survey. Four approaches were evaluated—CNNs, Dirichlet distribution, Monte Carlo dropout (MCD), and Bayesian neural networks with variational inference—using SDSS data and a 4MOST mock dataset. The MCD‑augmented CNN achieved the highest accuracies (92.6% on SDSS, 93.9% on mock data) while also delivering well‑calibrated uncertainty estimates with minimal extra computational cost.
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
The paper demonstrates that a self‑supervised Vision Transformer (ViT) pretrained on a fast, low‑cost semi‑numerical simulator can produce data summaries that transfer across different simulators without retraining. In 21cm cosmology, the ViT—named SKATR—pretrained on 67,000 21cmFAST lightcones is applied unchanged to hydrodynamical Loreli II lightcones, enabling accurate inference of five astrophysical parameters with fewer radiative‑transfer simulations than a fully‑supervised baseline. SKATR remains accurate, informative, and calibrated even under realistic SKA antenna array noise, outperforming supervised models retrained on noisy data.
arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.
arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.
The paper presents a physics-informed neural network (PINN) that accelerates multilayer spectral inversion (MLSI) of solar chromospheric lines Hα 6562.8 Å and Ca II 8542.1 Å. The PINN predicts MLSI parameters from observed line profiles and uses a differentiable forward model to synthesize spectra, trained in two stages—first with spectral reconstruction loss, then fine‑tuned with conventional MLSI results on a single reference image. Applied to Fast Imaging Solar Spectrograph data, the method reproduces key spatial structures and achieves a 12–60× speedup, processing a raster in 5–15 s versus 3–5 min for traditional MLSI.
arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.