Characterizing the Formation History of Milky Way Like Stellar Halos With Model Emulators

Gómez, Facundo A.; Coleman-Smith, Christopher E.; O’Shea, Brian W.; Tumlinson, Jason; Wolpert, Robert L.

doi:10.1088/0004-637x/760/2/112

Cited by 45 publications

(53 citation statements)

References 74 publications

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“…Due to the infeasibility of performing millions of full model runs, the MCMC procedure implemented an emulator in the place of the full model [2,3]. The emulator uses an interpolation algorithm to determine the observables from 1200 full-model runs.…”

Section: Markov Chain Monte Carlo Procedures and Resultsmentioning

confidence: 99%

“…It is faced in disparate fields of science such as biology, econometrics, and cosmology. The common solution to this problem is to apply a Bayesian inference approach where a posterior distribution over model parameters can be mapped out based on their consistency with a set of observables using a Markov Chain Monte Carlo (MCMC) or a related approach [1,2]. This allows for quantitative conclusions about the underlying parameters to be made given the validity of the model and prior assumptions.…”

Section: Introductionmentioning

confidence: 99%

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Toward a deeper understanding of how experiments constrain the underlying physics of heavy-ion collisions

Sangaline

Pratt

2016

Phys. Rev. C

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Recent work has provided the means to rigorously determine properties of super-hadronic matter from experimental data through the application of broad scale modeling of high-energy nuclear collisions within a Bayesian framework. These studies have provided unprecedented statistical inferences about the physics underlying nuclear collisions by virtue of simultaneously considering a wide range of model parameters and experimental observables. Notably, this approach has been used to constrain both the QCD equation of state and the shear viscosity above the quark-hadron transition. Although the inferences themselves have a clear meaning, the complex nature of the relationships between model parameters and observables have remained relatively obscure. We present here a novel extension of the standard Bayesian Markov Chain Monte Carlo approach that allows for the quantitative determination of how inferences of model parameters are driven by experimental measurements and their uncertainties. This technique is then applied in the context of heavy ion collisions in order to explore previous results in greater depth. The resulting relationships are useful for identifying model weaknesses, prioritizing future experimental measurements, and most importantly: developing an intuition for the role that different observables play in constraining our understanding of the underlying physics.2

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Section: Markov Chain Monte Carlo Procedures and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward a deeper understanding of how experiments constrain the underlying physics of heavy-ion collisions

Sangaline

Pratt

2016

Phys. Rev. C

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“…Both types of models are providing predictions about the distribution of observable quantities for galaxies-particularly Milky Way-type galaxies-in great detail. Recent examples include metallicity (Cooper et al 2010;Font et al 2011;Gómez et al 2012a;Tissera et al 2013Tissera et al , 2014, stellar chemical abundances (Font et al 2006), color profiles (Monachesi et al 2013), luminosity and radial distributions of satellite galaxies (Koposov et al 2008;Tollerud et al 2008;Wang et al 2013), and the degree of substructure in the phase space of the stellar halo (Gómez et al 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Several efforts are under way to develop the tools required for this enterprise (Henriques et al 2009;Bower et al 2010;Lu et al 2012Lu et al , 2013Gómez et al 2012a;Ruiz et al 2013). In most of these works, the main goal was the identification of the "best" set of input parameters, or a region of best fit, within which a given set of observations could be successfully reproduced by a specific model.…”

Section: Introductionmentioning

confidence: 99%

Dissecting Galaxy Formation Models With Sensitivity Analysis—a New Approach to Constrain the Milky Way Formation History

Gómez

Coleman-Smith

O’Shea

et al. 2014

ApJ

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We present an application of a statistical tool known as sensitivity analysis to characterize the relationship between input parameters and observational predictions of semi-analytic models of galaxy formation coupled to cosmological N-body simulations. We show how a sensitivity analysis can be performed on our chemo-dynamical model, ChemTreeN, to characterize and quantify its relationship between model input parameters and predicted observable properties. The result of this analysis provides the user with information about which parameters are most important and most likely to affect the prediction of a given observable. It can also be used to simplify models by identifying input parameters that have no effect on the outputs (i.e., observational predictions) of interest. Conversely, sensitivity analysis allows us to identify what model parameters can be most efficiently constrained by the given observational data set. We have applied this technique to real observational data sets associated with the Milky Way, such as the luminosity function of the dwarf satellites. The results from the sensitivity analysis are used to train specific model emulators of ChemTreeN, only involving the most relevant input parameters. This allowed us to efficiently explore the input parameter space. A statistical comparison of model outputs and real observables is used to obtain a "bestfitting" parameter set. We consider different Milky-Way-like dark matter halos to account for the dependence of the best-fitting parameter selection process on the underlying merger history of the models. For all formation histories considered, running ChemTreeN with best-fitting parameters produced luminosity functions that tightly fit their observed counterpart. However, only one of the resulting stellar halo models was able to reproduce the observed stellar halo mass within 40 kpc of the Galactic center. On the basis of this analysis, it is possible to disregard certain models, and their corresponding merger histories, as good representations of the underlying merger history of the Milky Way.

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“…This model can be both qualitatively and quantitatively compared to the observed abundance distributions in large stellar surveys, and we have designed our model outputs so that it will be straightforward to couple the model to sophisticated statistical tools (e.g., Gómez et al 2012Gómez et al , 2013. These tools will enable detailed, quantitative comparison of models to both current and future observations of Milky Way stellar populationsand are particularly useful when dealing with multiple large data sets (such as combinations of many different observables).…”

Section: Discussionmentioning

confidence: 99%

Tracing the Evolution of High-Redshift Galaxies Using Stellar Abundances

Crosby

O’Shea

Tumlinson

2016

ApJ

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This paper presents the first results from a model for chemical evolution that can be applied to N-body cosmological simulations and quantitatively compared to measured stellar abundances from large astronomical surveys. This model convolves the chemical yield sets from a range of stellar nucleosynthesis calculations (including asymptotic giant branchstars, Type Ia and II supernovae, and stellar wind models) with a user-specified stellar initial mass function (IMF) and metallicity to calculate the time-dependent chemical evolution model for a "simple stellar population" (SSP) of uniform metallicity and formation time. These SSP models are combined with a semianalytic model for galaxy formation and evolution that uses merger trees from N-body cosmological simulations to track several α-and iron-peak elements for the stellar and multiphase interstellar medium components of several thousand galaxies in the early (z 6) universe. The simulated galaxy population is then quantitatively compared to two complementary datasets of abundances in the Milky Way stellar haloand is capable of reproducing many of the observed abundance trends. The observed abundance ratio distributions arebest reproduced with a Chabrier IMF, a chemically enriched star formation efficiency of 0.2, and a redshift of reionization of 7. Many abundances are qualitatively well matched by our model, but our model consistently overpredicts the carbon-enhanced fraction of stars at low metallicities, likely owingto incomplete coverage of Population III stellar yields and supernova modelsand the lack of dust as a component of our model.

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Characterizing the Formation History of Milky Way Like Stellar Halos With Model Emulators

Cited by 45 publications

References 74 publications

Toward a deeper understanding of how experiments constrain the underlying physics of heavy-ion collisions

Toward a deeper understanding of how experiments constrain the underlying physics of heavy-ion collisions

Dissecting Galaxy Formation Models With Sensitivity Analysis—a New Approach to Constrain the Milky Way Formation History

Tracing the Evolution of High-Redshift Galaxies Using Stellar Abundances

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