scholarly journals Sub-damped Lyman α systems in the XQ-100 survey – II. Chemical evolution at 2.4 ≤ z ≤ 4.3

2021 ◽  
Vol 502 (3) ◽  
pp. 4009-4025
Author(s):  
Trystyn A M Berg ◽  
Michele Fumagalli ◽  
Valentina D’Odorico ◽  
Sara L Ellison ◽  
Sebastián López ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Gas Phase ◽  
Chemical Evolution ◽  
Ionic Species ◽  
Redshift Range ◽  
Monte Carlo Techniques ◽  
Detection Frequency ◽  
The Universe ◽  
Large Grid

ABSTRACT We present the measured gas-phase metal column densities in 155 sub-damped Ly α systems (subDLAs) with the aim to investigate the contribution of subDLAs to the chemical evolution of the Universe. The sample was identified within the absorber-blind XQ-100 quasar spectroscopic survey over the redshift range 2.4 ≤ zabs ≤ 4.3. Using all available column densities of the ionic species investigated (mainly C iv, Si ii, Mg ii, Si iv, Al ii, Fe ii, C ii, and O i; in order of decreasing detection frequency), we estimate the ionization-corrected gas-phase metallicity of each system using Markov chain Monte Carlo techniques to explore a large grid of cloudy ionization models. Without accounting for ionization and dust depletion effects, we find that the H i-weighted gas-phase metallicity evolution of subDLAs is consistent with damped Ly α systems (DLAs). When ionization corrections are included, subDLAs are systematically more metal poor than DLAs (between ≈0.5σ and ≈3σ significance) by up to ≈1.0 dex over the redshift range 3 ≤ zabs ≤ 4.3. The correlation of gas phase [Si/Fe] with metallicity in subDLAs appears to be consistent with that of DLAs, suggesting that the two classes of absorbers have a similar relative dust depletion pattern. As previously seen for Lyman limit systems, the gas phase [C/O] in subDLAs remains constantly solar for all metallicities indicating that both subDLAs and Lyman limit systems could trace carbon-rich ejecta, potentially in circumgalactic environments.

scholarly journals Typical representatives of free homotopy classes in multi-punctured plane

2019 ◽  
Vol 11 (03) ◽  
pp. 623-659
Author(s):  
Maxim Arnold ◽  
Yuliy Baryshnikov ◽  
Yuriy Mileyko
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Probability Measure ◽  
Homotopy Class ◽  
Piecewise Linear ◽  
Homotopy Classes ◽  
Simple Method ◽  
Monte Carlo Techniques ◽  
Free Homotopy Class

We show that a uniform probability measure supported on a specific set of piecewise linear loops in a nontrivial free homotopy class in a multi-punctured plane is overwhelmingly concentrated around loops of minimal lengths. Our approach is based on extending Mogulskii’s theorem to closed paths, which is a useful result of independent interest. In addition, we show that the above measure can be sampled using standard Markov Chain Monte Carlo techniques, thus providing a simple method for approximating shortest loops.

scholarly journals Implementation of a Markov Chain Monte Carlo Method to inorganic aerosol modeling of observations from the MCMA-2003 Campaign. Part II: Model application to the CENICA, Pedregal and Santa Ana sites

2006 ◽  
Vol 6 (4) ◽  
pp. 5999-6040
Author(s):  
F. M. San Martini ◽  
E. J. Dunlea ◽  
R. Volkamer ◽  
T. B. Onasch ◽  
J. T. Jayne ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Nitric Acid ◽  
Gas Phase ◽  
Mexico City ◽  
Monte Carlo Model ◽  
Rural Site ◽  
Inorganic Species ◽  
Inorganic Aerosol

Abstract. A Markov Chain Monte Carlo model for integrating the observations of inorganic species with a thermodynamic equilibrium model was presented in Part I of this series. Using observations taken at three ground sites, i.e. a residential, industrial and rural site, during the MCMA-2003 campaign in Mexico City, the model is used to analyze the inorganic aerosol and ammonia data and predict gas phase concentrations of nitric and hydrochloric acid. In general the model is able to accurately predict the observed inorganic aerosol concentrations at all three sites. The agreement between the predicted and observed gas phase ammonia concentration is excellent. The NOz concentration calculated from the NOy, NO and NO2 observations is of limited use in constraining the gas phase nitric acid concentration given the large uncertainties in this measure of nitric acid and additional reactive nitrogen species. Focusing on the acidic period of 9–11 April identified by Salcedo et al. (2006), the model accurately predicts the aerosol phase observations during this period with the exception of the nitrate predictions after 10:00 a.m. (CDT) on 9 April, where the model underpredicts the observations by, on average, 20%. For periods when the aerosol chloride observations are consistently above the detection limit, the model is able to both accurately predict the aerosol chloride predictions and provide well-constrained HCl (g) concentrations. When the aerosols are aqueous, the most likely concentrations of HCl (g) are in the sub-ppbv range. The most likely predicted concentration of HCl (g) was found to reach concentrations of order 10 ppbv if the aerosols are dry. Finally, the atmospheric relevance of HCl (g) is discussed in terms of its indicator properties for the possible influence of chlorine-mediated photochemistry in Mexico City.

scholarly journals A Confront between Amati and Combo Correlations at Intermediate and Early Redshifts

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1118
Author(s):  
Marco Muccino
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Predictive Power ◽  
Energy Equation ◽  
Gamma Ray ◽  
Zero Curvature ◽  
Level 3 ◽  
The Difference ◽  
Best Fit ◽  
The Universe

I consider two gamma-ray burst (GRB) correlations: Amati and Combo. After calibrating them in a cosmology-independent way by employing Beziér polynomials to approximate the Observational Hubble Dataset (OHD), I perform Markov Chain Monte Carlo (MCMC) simulations within the Λ CDM and the wCDM models. The results from the Amati GRB dataset do not agree with the standard Λ CDM model at a confidence level ≥ 3 – σ . For the Combo correlation, all MCMC simulations give best-fit parameters which are consistent within 1– σ with the Λ CDM model. Pending the clarification of whether the diversity of these results is statistical, due to the difference in the dataset sizes, or astrophysical, implying the search for the most suited correlation for cosmological analyses, future investigations require larger datasets to increase the predictive power of both correlations and enable more refined analyses on the possible non-zero curvature of the Universe and the dark energy equation of state and evolution.

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