DOI: 10.57760/sciencedb.j00167.00039
title: Appendix of the paper entitled "Chasing the formation history of the Galactic metal-poor disk".
title: Appendix of the paper entitled "Chasing the formation history of the Galactic metal-poor disk".

description: This appendix provides details on the estimation of chemical abundances. For the machine learning reference set, we used stars from Li et al. (2022a) and JINAbase Abohalima & Frebel (2018), Fig. A.1 presents the distribution of stellar parameters for the reference set and target stars. Noting systematic biases between the two sources. Fig. A.2 compares atmospheric parameters and chemical abundances for stars common to both sources, and Table A.1 lists parameters for revising abundances from JINAbase Abohalima & Frebel (2018), with details in Section 2.1.1. Table A.2 specifies the wavelength ranges used to estimate Fe, Mg, Ca, and C abundances via template fitting. Fig. A.3 shows machine learning accuracy on training and test sets, while Fig. A.4 illustrates the relationship between errors and prediction accuracy. Fig. A.6, A.7, A.8, and A.9 compare Fe, Mg, Ca, and C abundances measured by machine learning and template fitting, with subplots showing results with and without error filtering. We estimated the atmospheric parameters and chemical abundances of ∼ 100, 000 metal-poor stars using machine learning. For Fe, C, Mg, and Ca, we also conducted measurements through template fitting. To evaluate the accuracy of our results, we compared our values with those from Li et al. (2022b). Fig. A.5 presents the comparison between our machine learning results and those from Li et al. (2022b), while Fig. A.10 shows the comparison of template fitting results with values from Li et al. (2022b).
description: This appendix provides details on the estimation of chemical abundances. For the machine learning reference set, we used stars from Li et al. (2022a) and JINAbase Abohalima & Frebel (2018), Fig. A.1 presents the distribution of stellar parameters for the reference set and target stars. Noting systematic biases between the two sources. Fig. A.2 compares atmospheric parameters and chemical abundances for stars common to both sources, and Table A.1 lists parameters for revising abundances from JINAbase Abohalima & Frebel (2018), with details in Section 2.1.1. Table A.2 specifies the wavelength ranges used to estimate Fe, Mg, Ca, and C abundances via template fitting. Fig. A.3 shows machine learning accuracy on training and test sets, while Fig. A.4 illustrates the relationship between errors and prediction accuracy. Fig. A.6, A.7, A.8, and A.9 compare Fe, Mg, Ca, and C abundances measured by machine learning and template fitting, with subplots showing results with and without error filtering. We estimated the atmospheric parameters and chemical abundances of ∼ 100, 000 metal-poor stars using machine learning. For Fe, C, Mg, and Ca, we also conducted measurements through template fitting. To evaluate the accuracy of our results, we compared our values with those from Li et al. (2022b). Fig. A.5 presents the comparison between our machine learning results and those from Li et al. (2022b), while Fig. A.10 shows the comparison of template fitting results with values from Li et al. (2022b).