Machine Learning the Tip of the Red Giant Branch
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866911141705285632 |
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| author | Dennis, Mitchell Sakstein, Jeremy |
| author_facet | Dennis, Mitchell Sakstein, Jeremy |
| contents | 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 $ω$-Centauri.~The LMC and NGC 4258 measurements are consistent with other measurements within $<1σ$ errors, and the $ω$-Centauri measurement are within $<2σ$ errors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_12069 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine Learning the Tip of the Red Giant Branch Dennis, Mitchell Sakstein, Jeremy Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Solar and Stellar Astrophysics 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 $ω$-Centauri.~The LMC and NGC 4258 measurements are consistent with other measurements within $<1σ$ errors, and the $ω$-Centauri measurement are within $<2σ$ errors. |
| title | Machine Learning the Tip of the Red Giant Branch |
| topic | Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics Solar and Stellar Astrophysics |
| url | https://arxiv.org/abs/2303.12069 |