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Title: Uncertainties in Galactic Chemical Evolution Models

Journal Article · · The Astrophysical Journal (Online)
 [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Univ. of Victoria, BC (Canada); Michigan State Univ., East Lansing, MI (United States); Joint Inst. for Nuclear Astrophysics (JINA), East Lansing, MI (United States). Center for the Evolution of the Elements (JINA-CEE)
  2. Univ. of Victoria, BC (Canada); Joint Inst. for Nuclear Astrophysics (JINA), East Lansing, MI (United States). Center for the Evolution of the Elements (JINA-CEE)
  3. Joint Inst. for Nuclear Astrophysics (JINA), East Lansing, MI (United States). Center for the Evolution of the Elements (JINA-CEE); Michigan State Univ., East Lansing, MI (United States)
  4. Univ. of Victoria, BC (Canada)
  5. Univ. of Hull, Kingston upon Hull (United Kingdom); Hungarian Academy of Sciences, Budapest (Hungary). Konkoly Observatory
  6. Heidelberg Inst. for Theoretical Studies, Heidelberg (Germany)
  7. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

Here we use a simple one-zone galactic chemical evolution model to quantify the uncertainties generated by the input parameters in numerical predictions for a galaxy with properties similar to those of the Milky Way. We compiled several studies from the literature to gather the current constraints for our simulations regarding the typical value and uncertainty of the following seven basic parameters: the lower and upper mass limits of the stellar initial mass function (IMF), the slope of the high-mass end of the stellar IMF, the slope of the delay-time distribution function of Type Ia supernovae (SNe Ia), the number of SNe Ia per M⊙ formed, the total stellar mass formed, and the final mass of gas. We derived a probability distribution function to express the range of likely values for every parameter, which were then included in a Monte Carlo code to run several hundred simulations with randomly selected input parameters. This approach enables us to analyze the predicted chemical evolution of 16 elements in a statistical manner by identifying the most probable solutions along with their 68% and 95% confidence levels. Our results show that the overall uncertainties are shaped by several input parameters that individually contribute at different metallicities, and thus at different galactic ages. The level of uncertainty then depends on the metallicity and is different from one element to another. Among the seven input parameters considered in this work, the slope of the IMF and the number of SNe Ia are currently the two main sources of uncertainty. The thicknesses of the uncertainty bands bounded by the 68% and 95% confidence levels are generally within 0.3 and 0.6 dex, respectively. When looking at the evolution of individual elements as a function of galactic age instead of metallicity, those same thicknesses range from 0.1 to 0.6 dex for the 68% confidence levels and from 0.3 to 1.0 dex for the 95% confidence levels. The uncertainty in our chemical evolution model does not include uncertainties relating to stellar yields, star formation and merger histories, and modeling assumptions.

Research Organization:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396; W-7405-ENG-36; PHY-1430152; NNX12AC98G; HST-AR-13261.01-A
OSTI ID:
1334121
Report Number(s):
LA-UR-15-27074
Journal Information:
The Astrophysical Journal (Online), Vol. 824, Issue 2; ISSN 1538-4357
Publisher:
Institute of Physics (IOP)Copyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 33 works
Citation information provided by
Web of Science

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Cited By (19)

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NuGrid stellar data set – II. Stellar yields from H to Bi for stellar models with MZAMS = 1–25 M⊙ and Z = 0.0001–0.02 journal June 2018
Simulating an isolated dwarf galaxy with multichannel feedback and chemical yields from individual stars journal October 2018
The Gravitational waves merger time distribution of binary neutron star systems journal June 2019
Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models journal January 2017
Constraining the Final Fates of Massive Stars by Oxygen and Iron Enrichment History in the Galaxy journal January 2018
Validating Semi-analytic Models of High-redshift Galaxy Formation Using Radiation Hydrodynamical Simulations journal May 2018
Galactic Archeology with the AEGIS Survey: The Evolution of Carbon and Iron in the Galactic Halo journal July 2018
Explaining the Multiple Populations in Globular Clusters by Multiple Episodes of Star Formation and Enrichment without Gas Expulsion from Massive Star Feedback journal December 2018
Neutron Star Mergers Might Not Be the Only Source of r -process Elements in the Milky Way journal April 2019
Galactic Chemical Evolution of Radioactive Isotopes journal June 2019
Inferring Galactic Parameters from Chemical Abundances: A Multi-star Approach journal December 2019
SYGMA: Stellar Yields for Galactic Modeling Applications journal August 2018
Chemical trends in the Galactic Halo with APOGEE data text January 2016
NuGrid Stellar Data Set. II. Stellar Yields from H to Bi for Stellar Models with Mzams = 1 to 25Msun and Z = 0.0001 to 0.02 text January 2017
SYGMA: Stellar Yields for Galactic Modeling Applications text January 2017
Constraining the final fates of massive stars by oxygen and iron enrichment history in the Galaxy text January 2017
Explaining the multiple populations in globular clusters by multiple episodes of star formation and enrichment without gas expulsion from massive star feedback text January 2018
The Gravitational waves merger time distribution of binary neutron star systems text January 2019

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79 ASTRONOMY AND ASTROPHYSICS
Astronomy and Astrophysics
Galaxy abundances
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