Colour-Based Disentangling of Mira Variables and Ultra-Cool Dwarfs

TL;DR

This paper develops a color-based classification method using survey data to distinguish Mira variables from ultra-cool dwarfs with over 91% accuracy, addressing photometric misclassification issues.

Contribution

It introduces empirical color selection criteria that effectively separate red dwarfs, brown dwarfs, and Mira variables in large photometric surveys.

Findings

Achieved 91-92% classification accuracy.

Identified challenges in distinguishing Mira variables from brown dwarfs.

Validated robustness through bootstrap analysis.

Abstract

Despite having different astronomical characteristics, the studies of mira variables and ultra-cool dwarfs frequently show similar red colors, which could cause leading to photometric misclassification. This study uses photometric data from the WISE, 2MASS, and Pan-STARRS surveys to construct color-based selection criteria for red dwarfs, brown dwarfs, and Mira variables. On analyzing the color indices, we developed empirical rules that separate these objects with an overall classification accuracy of approximately 91%-92%. While the differentiation between red dwarfs and both Mira variables and brown dwarfs is effective, challenges remain in distinguishing Mira variables from brown dwarfs due to overlapping color indices. The robustness of our classification technique was validated by a bootstrap analysis, highlighting the significance of color indices in large photometric surveys for stellar classification.