On the detectability and parameterisation of binary stars through spectral energy distributions

TL;DR

This paper investigates the detectability and parameter estimation of unresolved binary stars using spectral energy distributions, proposing simplified blackbody models and Python tools to improve characterization accuracy in large-scale surveys.

Contribution

It introduces a simplified blackbody modeling approach and Python tools for analyzing binary star systems through SEDs, addressing challenges in parameter recovery and error estimation.

Findings

Blackbody models can serve as effective baselines for error estimation.

The study demonstrates the potential of SEDs in identifying unresolved binaries.

Proposed tools facilitate analysis of binary systems across various spectral data.

Abstract

This study examines the characterization of binary star systems using Spectral Energy Distributions (SEDs), a technique increasingly essential with the rise of large-scale astronomical surveys. Binaries can emit flux at different regions of the electromagnetic spectrum, making SEDs a valuable tool in identifying and characterising unresolved binary systems. However, fitting multi-component models to SEDs and recovering accurate stellar parameters remains challenging due to nonlinear fitting methods and inherent uncertainties in the data and the spectral models. In this work, a simplified approach was used to model stars as blackbodies and we tested the accuracy of parameter recovery from SEDs, particularly focusing on secondary stars. We explored a range of primary properties, filter sets and noise models. Special attention was given to two case studies: one examining the detection of unresolved binaries using Gaia XP spectra, and the other focusing on identifying hotter companions in binary systems using UV-IR SEDs. Although an analytic prescription for recoverability is not possible, we present a simplified model and the necessary Python tools to analyse any potential binary system. Finally, we propose using blackbody models as a baseline for error estimation in SED fitting, offering a potential method for measuring fitting errors and improving the precision of binary star characterisations.