Deriving Stellar Properties, Distances, and Reddenings using Photometry and Astrometry with BRUTUS

Abstract: We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photometric and astrometric data. We outline the statistical framework for deriving these quantities, its implementation, and various Galactic priors over the 3-D distribution of stars, stellar properties, and dust extinction (including $R_V$ variation). We establish a procedure to empirically calibrate MIST v1.2 isochrones by using open clusters to derive corrections to the effective temperatures and radii of the isochrones, which reduces systematic errors on the lower main sequence. We also describe and apply a method to estimate photometric offsets between stellar models and observed data using nearby, low-reddening field stars. We perform a series of tests on mock and real data to examine parameter recovery with MIST under different modeling assumptions, illustrating that brutus is able to recover distances and other stellar properties using optical to near-infrared photometry and astrometry. The code is publicly available at https://github.com/joshspeagle/brutus.

An Analytical Summary of BRUTUS: Photometric and Astrometric Inference of Stellar Properties

The paper presents BRUTUS, a novel open-source Python package crafted to estimate stellar properties, distances, and reddenings using photometric and astrometric data. Developed to address core challenges in Galactic astronomy, BRUTUS serves as a versatile tool for transforming 2-D positional data into comprehensive 3-D maps using large datasets from surveys such as the Sloan Digital Sky Survey (SDSS) and the Gaia mission. The complexities involved in deciphering the spatial distribution, chemical composition, and evolution of the Milky Way underpin the necessity for BRUTUS, which employs a Bayesian statistical framework enhanced by empirical adjustments for stellar isochrones.

Statistical Framework and Model Implementation

BRUTUS primarily hinges on a probabilistic approach that integrates photometric and astrometric data within a Bayesian framework, facilitating the estimation of stellar parameters manifested by intrinsic (e.g., mass, metallicity, age) and extrinsic (e.g., distance, extinction) nature. The statistical model assumes Gaussian uncertainties in photometric measures and parallax data, with priors addressing the initial mass function (IMF), 3-D stellar number density, and dust extinction variations modeled via the Rv parameter. The intrinsic parameters are informed by MESA Isochrones and Stellar Tracks (MIST) models, while the photometric data are attained from synthetic spectral simulations leveraging C3K atmospheric models.

Empirical Calibration of Isochrones

Recognizing the systematic deviations in theoretical models, the paper introduces a method to calibrate MIST isochrones using empirical corrections derived from open cluster data. This involves refining models of effective temperature and radius, crucially impacting the Main Sequence, especially in low-mass stars. These corrections are shown to mitigate the discrepancies found in lower mass stars due to processes such as magnetic field effects and radius inflation. The calibration process also incorporates photometric offsets established via high-fidelity parallax data, enabling consistency checks across observed and modeled spectrophotometric attributes.

Validation and Implications

The performance and robustness of BRUTUS are validated through both synthetic tests on mock data and comparative analysis with spectrophotometric distance measurements from the H3 survey. The inference of distances exclusive of parallax data attests to the efficacy of BRUTUS in retrieving unbiased parallax estimates commensurate with those from Gaia. Notably, the presence of a Galactic prior influences the inferred stellar parameters, especially concerning intrinsic biases imposed by the widely varying 3-D structures of the dust and star distribution.

The empirical results underscore the interdependence of various priors and suggest that the accurate recovery of stellar parameters is contingent on the availability of precise parallax measurements. This study emphasizes the importance of integrated photometric and astrometric approaches in contemporary astro-statistical endeavors.

Future Directions

The BRUTUS framework stands poised for expansion, offering potentials for incorporating more complex Galactic priors and improving the fidelity of dust extinction models. The inclusion of a diverse array of isochrones like PARSEC and BPASS will further augment the model’s applicability across a broader spectrum of stellar types and evolutionary states. This further underscores the potential of BRUTUS to adapt to future advancements in data acquisition and processing, marking it as a pivotal tool in the next phase of astronomical exploration.

The paper’s contribution lays foundational work for continued exploration of the Milky Way, indicating directions for future advancements, particularly in spectral and photometric survey interpretations. In summation, BRUTUS represents a significant step forward in modeling the 3-D Galactic structure, embodying both an advancement in computational astrophysics and a testament to collaborative research efforts shaping the understanding of our cosmic neighborhood.