Prediction of Fundamental Stellar Parameters of F Type Star- A Comprehensive Study using Bayesian Neural Network.
Year: 2025
Type: article
Abstract: Accurate determination of stellar parameters is crucial for understandingstellar evolution, exoplanet habitability, and chemical peculiarities in stellaratmospheres. Our work aims to improve the precision of predicting stellarproperties—effective temperature (Teff), metallicity ([Fe/H]), and surfacegravity (log g)—by leveraging advanced machine learning techniques. [1]We employ a medium-density neural network with a non-linear activationfunction, trained using a dataset of 588 F-type stars selected from theXSHOOTER, [2] ELODIE 3.0, MILES, and Indo-US stellar libraries. [3]Focusing on hydrogen Balmer lines and metallic lines in the visible range(390–550 nm), as given by the LICK/ADS database, [4] we reprocess stellarspectra through pseudo-continuum normalization, equivalent widthmeasurements, and model fitting using the SPECTRUM code within theiSpec library. [5] Given the limited and potentially noisy nature of thedataset, Bayesian regularization [6] is implemented to prevent overfittingand enhance generalization. Our study also investigates the spectralevolution of F-type stars across their population distribution—spanningdwarfs, subgiants, and giants—providing insight into how key spectralfeatures change with stellar age and composition. Since F-type stars sharemany properties with G- and K-type stars, which are known to hosthabitable zones, a refined understanding of their stellar characteristics isessential for exoplanet studies. Moreover, our model can identify chemicallypeculiar F-type stars, [7] particularly those in pre-main-sequence stages, bycorrelating fundamental parameters with spectral line strengths. Byimproving the precision of stellar parameter determination, our workcontributes to a deeper understanding of F-type stars, their potential forhosting habitable planets, and the broader implications for stellarastrophysics
Author: Chakraborty, Avirup
Institution: S.N. Bose National Centre for Basic Sciences
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911767799
FWCI:
Cites:
Cited by:
Related to: