scholarly journals Star formation law in the epoch of reionization from [C ii] and C iii] lines

2020 ◽  
Vol 495 (1) ◽  
pp. L22-L26 ◽  
Author(s):  
L Vallini ◽  
A Ferrara ◽  
A Pallottini ◽  
S Carniani ◽  
S Gallerani
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Star Formation ◽  
Markov Chain Monte Carlo ◽  
Star Formation Rate ◽  
Formation Rate ◽  
Average Density ◽  
Monte Carlo Algorithm ◽  
James Webb Space Telescope ◽  
Novel Method

ABSTRACT We present a novel method to simultaneously characterize the star formation law and the interstellar medium properties of galaxies in the epoch of reionization (EoR) through the combination of [C ii] 158 μm (and its known relation with star formation rate) and C iii] λ1909 Å emission line data. The method, based on a Markov chain Monte Carlo algorithm, allows us to determine the target galaxy average density, n, gas metallicity, Z, and ‘burstiness’ parameter, κs, quantifying deviations from the Kennicutt–Schmidt relation. As an application, we consider COS-3018 (z = 6.854), the only EoR Lyman Break Galaxy so far detected in both [C ii] and C iii]. We show that COS-3018 is a moderate starburst (κs ≈ 3), with $Z \approx 0.4 \, \mathrm{Z}_{\odot }$, and $n \approx 500\, {\rm cm^{-3}}$. Our method will be optimally applied to joint ALMA and James Webb Space Telescope targets.

scholarly journals Fast Compression of MCMC Output

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1017
Author(s):  
Nicolas Chopin ◽  
Gabriel Ducrocq
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Equality Constraints ◽  
Survey Sampling ◽  
Monte Carlo Algorithm ◽  
Control Variates ◽  
Novel Method

We propose cube thinning, a novel method for compressing the output of an MCMC (Markov chain Monte Carlo) algorithm when control variates are available. It allows resampling of the initial MCMC sample (according to weights derived from control variates), while imposing equality constraints on the averages of these control variates, using the cube method (an approach that originates from survey sampling). The main advantage of cube thinning is that its complexity does not depend on the size of the compressed sample. This compares favourably to previous methods, such as Stein thinning, the complexity of which is quadratic in that quantity.

scholarly journals An efficient hybrid method to produce high-resolution large-volume dark matter simulations for semi-analytic models of reionization

2020 ◽  
Vol 500 (1) ◽  
pp. 493-505
Author(s):  
Yisheng Qiu ◽  
Simon J Mutch ◽  
Pascal J Elahi ◽  
Rhys J J Poulton ◽  
Chris Power ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Star Formation ◽  
High Resolution ◽  
Large Volume ◽  
Small Volume ◽  
Star Formation Rate ◽  
Formation Rate ◽  
Mass Resolution ◽  
Monte Carlo Algorithm ◽  
Low Resolution

ABSTRACT Resolving faint galaxies in large volumes is critical for accurate cosmic reionization simulations. While less demanding than hydrodynamical simulations, semi-analytic reionization models still require very large N-body simulations in order to resolve the atomic cooling limit across the whole reionization history within box sizes ${\gtrsim}100 \, h^{-1}\, \rm Mpc$. To facilitate this, we extend the mass resolution of N-body simulations using a Monte Carlo algorithm. We also propose a method to evolve positions of Monte Carlo haloes, which can be an input for semi-analytic reionization models. To illustrate, we present an extended halo catalogue that reaches a mass resolution of $M_\text{halo} = 3.2 \times 10^7 \, h^{-1} \, \text{M}_\odot$ in a $105 \, h^{-1}\, \rm Mpc$ box, equivalent to an N-body simulation with ∼68003 particles. The resulting halo mass function agrees with smaller volume N-body simulations with higher resolution. Our results also produce consistent two-point correlation functions with analytic halo bias predictions. The extended halo catalogues are applied to the meraxes semi-analytic reionization model, which improves the predictions on stellar mass functions, star formation rate densities, and volume-weighted neutral fractions. Comparison of high-resolution large-volume simulations with both small-volume and low-resolution simulations confirms that both low-resolution and small-volume simulations lead to reionization ending too rapidly. Lingering discrepancies between the star formation rate functions predicted with and without our extensions can be traced to the uncertain contribution of satellite galaxies.

scholarly journals Calibrating the James Webb Space Telescope Filters as Star Formation Rate Indicators

2018 ◽  
Vol 869 (2) ◽  
pp. L26 ◽  
Author(s):  
Madhooshi R. Senarath ◽  
Michael J. I. Brown ◽  
Michelle E. Cluver ◽  
John Moustakas ◽  
Lee Armus ◽  
...  
Keyword(s):  
Star Formation ◽  
Star Formation Rate ◽  
Formation Rate ◽  
Space Telescope ◽  
James Webb Space Telescope

scholarly journals Estimation of trace gas fluxes with objectively determined basis functions using reversible-jump Markov chain Monte Carlo

2016 ◽  
Vol 9 (9) ◽  
pp. 3213-3229 ◽  
Author(s):  
Mark F. Lunt ◽  
Matt Rigby ◽  
Anita L. Ganesan ◽  
Alistair J. Manning
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Space ◽  
Spatial Domain ◽  
Monte Carlo Algorithm ◽  
Trace Gas ◽  
Reversible Jump ◽  
Pseudo Data ◽  
Atmospheric Trace Gas

Abstract. Atmospheric trace gas inversions often attempt to attribute fluxes to a high-dimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of under-determination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the well-established reversible-jump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a single-step process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversible-jump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudo-data example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over north-west Europe to be resolved.

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