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. The preprocessing pipeline includes per-spectrum standardization, RobustScaler normalization of the target variables -- effective temperature $T_{\mathrm{eff}}$, metallicity $[\mathrm{Fe/H}]$, and surface gravity $\log g$ -- and data augmentation via Gaussian noise injection. On a held-out test se
The continuous growth in AI methodologies, combined with increasing astronomical data availability like that from SDSS, enables the development of advanced tools for astrophysical analysis.
This development allows for more precise and automated characterization of stars, which is fundamental for understanding galactic evolution and exoplanet host stars.
The use of residual multitask neural networks provides a more robust and efficient method for stellar parameter estimation compared to traditional or simpler machine learning approaches.
- · Astrophysicists
- · Machine Learning Researchers
- · Space observatories
- · Traditional spectroscopic analysis methods
More accurate and faster classification of stellar properties.
Improved understanding of star formation, galactic structure, and stellar evolution models.
Enhanced ability to filter and analyze large astronomical datasets for identifying unusual celestial objects or phenomena.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG