deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning

Autor:	Gilda, Sankalp
Rok vydání:	2023
Předmět:	Astrophysics - Solar and Stellar Astrophysics Astrophysics - Astrophysics of Galaxies Astrophysics - Instrumentation and Methods for Astrophysics Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	Traditional spectral analysis methods are increasingly challenged by the exploding volumes of data produced by contemporary astronomical surveys. In response, we develop deep-Regularized Ensemble-based Multi-task Learning with Asymmetric Loss for Probabilistic Inference ($\rm{deep-REMAP}$), a novel framework that utilizes the rich synthetic spectra from the PHOENIX library and observational data from the MARVELS survey to accurately predict stellar atmospheric parameters. By harnessing advanced machine learning techniques, including multi-task learning and an innovative asymmetric loss function, $\rm{deep-REMAP}$ demonstrates superior predictive capabilities in determining effective temperature, surface gravity, and metallicity from observed spectra. Our results reveal the framework's effectiveness in extending to other stellar libraries and properties, paving the way for more sophisticated and automated techniques in stellar characterization. Comment: 5 main pages + 2 figures. Accepted to the ML4PS workshop at NeurIPS 2023
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.03738 Zobrazit plný text záznamu View this record from Arxiv