scholarly journals Exploiting the transit timing capabilities of Ariel

2021 ◽  
Author(s):  
Luca Borsato ◽  
Valerio Nascimbeni ◽  
Giampaolo Piotto ◽  
Gyula Szabó
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Transit Time ◽  
Planetary System ◽  
Mass Range ◽  
Monte Carlo Analysis ◽  
Light Curves ◽  
Timing Errors ◽  
Time Uncertainty ◽  
Dynamical Simulations

AbstractThe Transit Timing Variation (TTV) technique is a powerful dynamical tool to measure exoplanetary masses by analysing transit light curves. We assessed the transit timing performances of the Ariel Fine Guidance Sensors (FGS1/2) based on the simulated light curve of a bright, 55 Cnc, and faint, K2-24, planet-hosting star. We estimated through a Markov-Chain Monte-Carlo analysis the transit time uncertainty at the nominal cadence of 1 second and, as a comparison, at a 30 and 60-s cadence. We found that at the nominal cadence Ariel will be able to measure the transit time with a precision of about 12s and 34s, for a star as bright as 55 Cnc and K2-24, respectively. We then ran dynamical simulations, also including the Ariel timing errors, and we found an improvement on the measurement of planetary masses of about 20-30% in a K2-24-like planetary system through TTVs. We also simulated the conditions that allow us to detect the TTV signal induced by an hypothetical external perturber within the mass range between Earth and Neptune using 10 transit light curves by Ariel.

scholarly journals The effects of LIGO detector noise on a 15-dimensional Markov-chain Monte Carlo analysis of gravitational-wave signals

2010 ◽  
Vol 27 (11) ◽  
pp. 114009 ◽  
Author(s):  
V Raymond ◽  
M V van der Sluys ◽  
I Mandel ◽  
V Kalogera ◽  
C Röver ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Gravitational Wave ◽  
Monte Carlo Analysis ◽  
Detector Noise

scholarly journals Potential sources of red snapper (Lutjanus campechanus) recruits estimated with Markov Chain Monte Carlo analysis of otolith chemical signatures

2016 ◽  
Vol 67 (7) ◽  
pp. 992 ◽  
Author(s):  
Beverly K. Barnett ◽  
William F. Patterson ◽  
Todd Kellison ◽  
Steven B. Garner ◽  
Alan M. Shiller
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Chemical Constituents ◽  
Monte Carlo Analysis ◽  
Red Snapper ◽  
Lutjanus Campechanus ◽  
Age Classes ◽  
Elemental Ratios ◽  
Chemical Signatures

Otolith chemical signatures were used to estimate the number of likely nursery sources that contributed recruits to a suite of red snapper (Lutjanus campechanus) year-classes sampled in 2012 in US Atlantic Ocean waters from southern Florida (28°N) to North Carolina (34°N). Otoliths from subadult and adult fish (n=139; ages 2–5 years) were cored and their chemical constituents analysed for δ13C, δ18O, as well as the elemental ratios of Ba:Ca, Mg:Ca, Mn:Ca and Sr:Ca. Results from multiple linear regression analyses indicated that there was significant latitudinal variation for δ13C, Ba:Ca, Mg:Ca and Mn:Ca. Therefore, these variables were used to parameterise Markov Chain Monte Carlo (MCMC) models computed to estimate the most likely number of nursery sources to each age class. Results from MCMC models indicated that between two and seven nursery sources were equally plausible among the four age classes examined, but the likely number of nursery sources declined for fish aged 4 and 5 years because of apparent mixing between more northern and more southern signatures. Overall, there is evidence to reject the null hypothesis that a single nursery source contributed recruits among the age classes examined, but increased sample size from a broader geographic range may be required to refine estimates of the likely number of nursery sources.

Sensitivity analysis of an urban stormwater microorganism model

2010 ◽  
Vol 62 (6) ◽  
pp. 1393-1400 ◽  
Author(s):  
D. T. McCarthy ◽  
A. Deletic ◽  
V. G. Mitchell ◽  
C. Diaper
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Analysis ◽  
Parameter Distribution ◽  
Model Parameters ◽  
Urban Stormwater ◽  
Urban Catchments ◽  
Two Parameters

This paper presents the sensitivity analysis of a newly developed model which predicts microorganism concentrations in urban stormwater (MOPUS—MicroOrganism Prediction in Urban Stormwater). The analysis used Escherichia coli data collected from four urban catchments in Melbourne, Australia. The MICA program (Model Independent Markov Chain Monte Carlo Analysis), used to conduct this analysis, applies a carefully constructed Markov Chain Monte Carlo procedure, based on the Metropolis-Hastings algorithm, to explore the model's posterior parameter distribution. It was determined that the majority of parameters in the MOPUS model were well defined, with the data from the MCMC procedure indicating that the parameters were largely independent. However, a sporadic correlation found between two parameters indicates that some improvements may be possible in the MOPUS model. This paper identifies the parameters which are the most important during model calibration; it was shown, for example, that parameters associated with the deposition of microorganisms in the catchment were more influential than those related to microorganism survival processes. These findings will help users calibrate the MOPUS model, and will help the model developer to improve the model, with efforts currently being made to reduce the number of model parameters, whilst also reducing the slight interaction identified.

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