Study on Star Formation History of Nearby Galaxies: A Bayesian Approach

By : Soumojit Das , Tanuka Chattopadhyay , Asis K. Chattopadhyay , Partha Lahiri

Page No: 26-63

Abstract
Star formation scenario in galaxies of various morphological types is significant in the sense that it characterises the structure formation in the universe. Star
formation Rate (SFR) is an important index to study the above phenomenon. But direct measurement of SFR is im- possible as one has to accurately count stars formed per year in a galaxy. In this paper, we investigate the star formation by first forming homogeneous clusters of the galaxies using Gaussian Mixture Model Based Clustering technique (GMMBC) applied on a large data set of galaxies in the Local Volume (LV) and then predicting the star formation rate (SFR) within each cluster using Bayesian LASSO and Bayesian Linear-Regression Analysis techniques. Our investigation reveals five homogeneous clusters of galaxies having different star formation scenaria. Two of them are massive, disc-dominated, highly rotating, forming stars with highest rates and free from the influence of environment while other two have lower SFR, probably turbulence resisting the process of star formation. The remaining one is a cluster of dwarf galaxies where star formation is affected by high density environment. Our cluster specific predictive model uses a number of relevant auxiliary predictor variables in order to improve on the power in predicting SFR for newly discovered galaxies. We cross validate the SFRs in the various coherent clusters and compare with other (physical) model-based SFR values ( 𝑆𝐹𝑅 , say) assumed to be reasonable representative of true SFRs derived and discussed by various authors.

Authors
Soumojit Das: Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
Tanuka Chattopadhyay: University of Calcutta, Kolkata, India
Asis K. Chattopadhyay: University of Calcutta, Kolkata, India
Partha Lahiri: University of Maryland, College Park, USA
 

DOI: https://doi.org/10.32381/IAPQRT.2026.50.01.2