Machine Learning the Tip of the Red Giant Branch

Abstract

A method for investigating the sensitivity of the tip of the red giant branch (TRGB) I band magnitude $M_I$ to stellar input physics is presented.~We compute a grid of $\sim$125,000 theoretical stellar models with varying mass, initial helium abundance, and initial metallicity, and train a machine learning emulator to predict $M_I$ as a function of these parameters.~First, our emulator can be used to theoretically predict $M_I$ in a given galaxy using Monte Carlo sampling.~As an example, we predict $M_I = -3.87^{+0.11}_{-0.08}$ in the Large Magellanic Cloud (F20).~Second, our emulator enables a direct comparison of theoretical predictions for $M_I$ with empirical calibrations to constrain stellar modeling parameters using Bayesian Markov Chain Monte Carlo methods.~We demonstrate this by using empirical TRGB calibrations to obtain new independent measurements of the metallicity in three galaxies.~We find $\log_{10}(Z)=-2.167^{+0.404}_{-0.492}$ and $\log_{10}(Z)=-2.098^{+0.388}_{-0.528}$ in the Large Magellanic Cloud (F20 and Y19 respectively), $\log_{10}(Z)=-2.146^{+0.400}_{-0.505}$ in NGC 4258, and $\log_{10}(Z)=-2.143^{+0.401}_{-0.508}$ in $\omega$-Centauri.~The LMC and NGC 4258 measurements are consistent with other measurements within $<1\sigma$ errors, and the $\omega$-Centauri measurement are within $<2\sigma$ errors.