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Abstract Cool, dusty interstellar material plays an important role in the chemical evolution of galaxies. We present an analysis of this material across galaxy type through a spatially resolved spectral stacking analysis of galaxies from the MaNGA survey. With stellar population synthesis, we isolate neutral gas signals from resonance lines, comparing outcomes across model types, galactic geometry, and host stellar mass and age. We find that both synthetic and empirical models fail to capture the range of galactic chemical abundances. There is also notable Naicontamination from the Galaxy’s interstellar medium (ISM) in the MILES empirical stellar library. We are unable to reliably determine the column density of the gas due to the accuracy of absorption measurements, but differential analysis across radius and inclination reveals consistent and significant path-length dependent absorption in the equivalent width of Nai. We note similar but lesser trends in a narrow Caiiindex. We find no trends in Caior in a broad Caiiindex, indicating its ISM insensitivity and providing evidence in favor of its utility in determining the age and chemical content of stellar populations. Our data shows there is a cool ISM component in most external galaxies withDn(4000) < 1.7 that can be traced by Nai. Lastly, we caution that the characterization of gas kinematics traced by Naiin such low-resolution spectra is subject to systematic effects due to the chosen approach to stellar population modeling.
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Scalable stellar evolution forecasting: Deep learning emulation versus hierarchical nearest-neighbor interpolation
Many astrophysical applications require efficient yet reliable forecasts of stellar evolution tracks. One example is population synthesis, which generates forward predictions of models for comparison with observations. The majority of state-of-the-art rapid population synthesis methods are based on analytic fitting formulae to stellar evolution tracks that are computationally cheap to sample statistically over a continuous parameter range. The computational costs of running detailed stellar evolution codes, such as MESA, over wide and densely sampled parameter grids are prohibitive, while stellar-age based interpolation in-between sparsely sampled grid points leads to intolerably large systematic prediction errors. In this work, we provide two solutions for automated interpolation methods that offer satisfactory trade-off points between cost-efficiency and accuracy. We construct a timescale-adapted evolutionary coordinate and use it in a two-step interpolation scheme that traces the evolution of stars from zero age main sequence all the way to the end of core helium burning while covering a mass range from 0.65 to 300M⊙. The feedforward neural network regression model (first solution) that we train to predict stellar surface variables can make millions of predictions, sufficiently accurate over the entire parameter space, within tens of seconds on a 4-core CPU. The hierarchical nearest-neighbor interpolation algorithm (second solution) that we hard-code to the same end achieves even higher predictive accuracy, the same algorithm remains applicable to all stellar variables evolved over time, but it is two orders of magnitude slower. Our methodological framework is demonstrated to work on the MESA ISOCHRONES ANDSTELLARTRACKS(Choi et al. 2016) data set, but is independent of the input stellar catalog. Finally, we discuss the prospective applications of these methods and provide guidelines for generalizing them to higher dimensional parameter spaces.
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- Award ID(s):
- 2206523
- PAR ID:
- 10631865
- Publisher / Repository:
- Astronomy & Astrophysics
- Date Published:
- Journal Name:
- Astronomy & Astrophysics
- Volume:
- 681
- ISSN:
- 0004-6361
- Page Range / eLocation ID:
- A86
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1051/0004-6361/202347118
