← Back to all news
arXiv Machine Learning By Bruno Santos Meneses Barreto, Marcio Eisencraft

Multi-Variable Stellar Parameter Estimation Using Residual Multitask Neural Networks

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jun 24

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

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.

By David S. Duque-Casta\~no, Lauren Flor-Torres, Jorge I. Zuluaga
safety
More like this →
arXiv Machine Learning
Sep 14

4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

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.

By Deifilia Kieckhefen, Juan Pedro Guti\'errez Hermosillo Muriedas, Lars Helge Heyen, Mathis Bode, Iida Hakulinen, Andreas Herten, Chelsea Maria John, Thorsten Kurth, Anni Moisala, Asena Karolin \"Ozdemir, Kaleb Phipps, Oskar Taubert, Arvid Weyrauch, Markus G\"otz, Charlotte Debus
llmsbenchmarks
More like this →
arXiv Machine Learning
Sep 21

Bayesian classification of astronomical spectra with class uncertainties

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.

By Simon Barton, Martin Sahl\'en, Andreas Korn, Christian Glaser
More like this →