Setllar Population of Stellar Halos from MaNGA

This repository contains the scripts to conduct the analysis and generate the figures in Zhang et al. 2025 The data catalogs are stored under path ./data and the notebooks for reproducing figures are in ./plot_scripts Analysis Pipeline 1. Cross-match between MaNGA and SGA catalogs: match_manga.py; - product: ./data/manga_sga_z.fits 2. Measure stellar mass and effective radii: nsa_v_sga.py; - product: ./data/sga_mass_new_28mag2.fits 3. Construct sample mask: masks.py; - product: sample_mask_new2.fits 4. obtain additional masking after visual inspection: masking.py - product: ./data/masking.fits 6. Collect kinematic data and stellar mass of the final sample: kinematics.py - product: ./data/parameters_reff2_2.fits 7. split sample using different parameter spaces: sample_split.py - product: ./data/sample_split_outliers_total2.fits 8. Integrate spectra in 3 radial bins for each galaxy in the final sample: stack_gal.py - product: ./data/stacked_spec.fits 9. correct for telluric lines: sigma_unite.py (broaden the spectra to extract line features)+ fix_masks.py (compare with telluric line list and mask out pixels with large residuals around known telluric line features) - product: ./data/stacked_sigfix_mask.fits 10. smooth masked spectra to uniform resolution: smooth_spec.py - product: stacked_smooth_sigma300_mask_new2.fits 11. stack spectra for different sample-split methods and measure LICK indices (& uncertainties) and estimate measurement uncertainties for stacked spectra: uncertainty.py - product: ./data/index/*; ./data/std*.fits 12. measure LICK index values for sMILES SSPs - product: ./data/smiles_ssp/* 13. construct input spectra for alf fitting: make_spec.py - product: ./data/alf/* ---------------------------------------------------------- other scripts - mclrs.py: collection of several Mass-to-Light-ratio-to-Color Relations - calc_kcor.py: k-correction code - smiles_index.py: convolve sMILES SSP to data resolution and measure their LICK indices - products: - ./data/smiles_ssp/Bimodal_2.8_ssp.csv - ./data/smiles_ssp/Universal_Kroupa_ssp.csv - ./plot_scripts/read_alf.py: reading alf output data by Charlie Conroy, repository: alf - ./plot_scripts/ks-error.py: perform k-s test and estimate p value errors by bootstrapping Plotting Scripts path: ./plot_scripts - ./plot_scripts/plot_split.ipynb: visualize sample-split methods and the corresponding physical parameter distributions, Figure 4 & 5 - ./plot_scripts/plot_index.ipynb: plot radial profiles of LICK indices for different sample-split methods, Figure 6 & 7 - ./plot_scripts/simle_plot.ipynb: plot data index values against sMILES SSP grids, Figure 8 - ./plot_scripts/plot_alf_spectra.ipynb: compare data with output spectra from alf-fitting, Figure 9 - ./plot_scripts/plot_alf_corner.ipynb: generate corner plots for alf posteriors, Figure 10 - ./plot_scripts/plot_alf_gradient.ipynb: plot radial profiles of elemental abundances from alf fitting, Figure 11 & 12 & 18 & 19 - ./plot_scripts/plot_alf_gradient_imf.ipynb: compare alf results between Kroupa IMF and IMF-variable fitting, Figure 13